Unconventional oil and gas resources serve as vital replacement energy in China's hydrocarbon portfolio,and their efficient development is of great significance for safeguarding national energy security.The implementation of staged multi-clus-ter hydraulic fracturing in horizontal wells,along with the optimization of intra-stage cluster design parameters,is critical to maximizing the production potential of unconventional reservoirs.Clarifying fracture propagation mechanisms and quantifying the relationship between fracture geometry and well productivity is key to optimize intra-stage multi-cluster fracturing strategies.In this study,a phase-field method is employed to simulate the competitive propagation morphology of multiple fractures within a fracturing stage.A fracture morphology identification technique is integrated to construct a two-dimensional equivalent fracture model,which can characterize the stimulated flow pathways.Equivalent physical parameters after stimulation are extracted and transferred-together with geometric descriptors-as input for a discrete fracture flow model.This enables automatic coupling and data transfer between the geometric and flow models,thereby facilitating quantitative evaluation of production performance under different fracturing scenarios and ultimately achieving fully coupled fracture propagation-fluid flow simulation.The accuracy and feasibility of the dual-model coupling method are verified through comparison with laboratory-scale physical simulation experiments and field fracturing data.On this basis,the effects of intra-stage cluster number and cluster spacing on fracture morphology and production response are further investigated.The results show that,as the cluster spacing increases from 15 m to 25 m,the fracture deflection point shifts farther from the wellbore,and the tip deflection angle decreases from 30° to 24°.Meanwhile,the pressure gradient around the fracture tip is reduced,weakening the fluid driving force and significantly diminishing inter-fracture fluid interference.This change leads to a decline in peak daily oil production and stabilized production rate,with daily and cumulative oil output decreasing by 35.88%and 35.89%,respectively.In contrast,when the number of clusters per stage increases from 3 to 5,the deflection angle at the tip of the outer fractures increases from 30° to 34°,while the coverage of the induced stress field expands from 36.74%to 42.46%.This results in a higher pressure gradient surrounding the fractures,enhancing the fluid driving force and significantly improving oil mobilization.Consequently,peak daily and cumulative oil production increased by 40.49%and 45.467%,respectively.Therefore,optimizing the intra-stage cluster spacing and cluster number can effectively balance the degree of fracture interference and enhance single-well productivity,thereby improving the overall effectiveness of staged multi-cluster hydraulic fracturing in horizontal wells.

As a critical technical approach for shale reservoir development,dynamic imbibition displacement during the fractur-ing stage has emerged as a focal point in reservoir engineering research over recent years.In light of global energy demands and ongoing exploration of unconventional oil and gas resources,the significance of this technology in enhancing the exploitation of shale oil reservoirs cannot be overstated.However,the specific mechanisms of dynamic imbibition process in shale oil reservoirs influenced by various factors still aren't unclear,and it's difficult to accurately quantify their impact on imbibition oil production efficiency.These uncertainties significantly hinder further improvement in the development efficiency of shale oil reservoirs,lead to higher development costs and bring huge challenges to sustainable resource development. Aiming at the unclear dynamic imbibition mechanisms and action laws of shale oil reservoir,a core-scale numerical simulation model was established,and the control variable method was adopted to set up 15 simulation schemes.By these methods,the mechanisms of displacement pressure difference,capillary radius,wetting angle and oil-water viscosity of dynamic imbibition displacement effect,and the change laws of fluid seepage were revealed.The effects of displacement pressure difference,capillary radius,wetting angle,and oil-water viscosity on the effectiveness of dynamic imbibition oil recovery,and the laws of fluid seepage changes were clarified in this study.The results show that:During dynamic imbibition,as the capillary radius increase from 0.1 μm to 10 μm,capillary force decrease and fluid seepage rate accelerates,leading to 8.0%increase in imbibition recovery.Along with the displacing pressure difference increases from 0 MPa to 3 MPa,the imbibition upgrades from static to dynamic,and the imbibition recovery degree increases by 7.9%.It is considered that the displacing pressure difference and the recovery degree are in accordance with the power function relationship,and there is an optimal displacing pressure difference.With changes in rock wettability from hydrophilic to neutral or oleophilic,extraction degree decreases from 48.9%for water-wet conditions to 33.9%for oil-wet conditions.As crude oil viscosity decreases from 53.3 mPa·s to 13.99 mPa·s,imbibition recovery rate increases by 9.1%;the higher the viscosity of water phase,the smaller the initial imbibition velocity,but the better the imbibition displacement effect.In oil field operation,by optimizing injection pressure,selecting suitable fracturing fluid and surfactant,the hydrophilic degree and displacement phase viscosity can be improved,and the dynamic imbibition process can be improved to increase the oil displacement efficiency.In the future,the complexity of multiphase flows and the heterogeneity of reservoirs should be further considered to study the influence of various factors on the dynamic imbibition process of shale from different scales.

In order to investigate the deformation characteristic and transport behavior of oil-water micro-interface and its evolution law under different wettability conditions in water flooding,a Hele-Shaw cylindrical model has been constructed based on the N-S equation.Phase field method has been employed to track the topological deformation characteristics of oil-water micro-interface in water flooding.The effect of wettability,oil-water viscosity ratio,and capillary number on the deformation characteristic and evolution process of oil-water micro-interfaces has been studied.The simulation results show that the dynamic evolution process of oil-water micro-interfaces observed from the model surface in water flooding can be divided into four stag-es,including breakthrough,fracture,three-phase contact line intersection,and micro-interface merging.The breakthrough and fracture phenomenon of oil-water micro-interfaces can be observed repeatedly in the displacement process,and is not affected by wettability and rock particle distribution.Three-phase contact line intersection and micro-interface merging phenomenon have the similar deformation characteristics and evolution law in the vertical profile of the model,which are mainly influenced by wet-tability and rock particle distribution.Three-phase contact line intersection phenomenon occurs more frequently under water-wet condition,while the micro-interface merging phenomenon occurs more frequently under oil-wet condition.The change amplitude of displacement front decreases and then increases in water flooding as wettability changes from strong water-wet to strong oil-wet,which exhibits the piston-like displacement under weak water-wet condition.The simulation results show that the highest oil displacement efficiency is observed under weak water-wet condition,while the lowest oil displacement efficiency(61.06%)is observed under strong oil-wet condition.Moreover,as the oil-water viscosity ratio increases from 20 to 100,the occurrence rate of three-phase contact line intersection phenomenon decreases,the micro oil displacement efficiency decreases by 8.56%,and the initial displacement pressure also increases under weak water-wet and the same injected pore volume multiple condition.As the capillary number increases from 0.66×10-3 to 2.0×10-3,the occurrence rate of three-phase contact line intersection phenomenon increases,the volumes of residual oil decreases,the micro oil displacement efficiency increases by 9.36%,and the displacement pressure also decreases under weak water-wet and the same injected pore volume multiple condition.This reveals that the micro oil displacement efficiency can be significantly improved by increasing the occurrence rate of three-phase contact line intersection phenomenon under water-wet condition.The research results can enrich the micro flow mechanism in water flooding,and provide a theoretical basis for further explore and utilize the residual oil.

Rock spontaneous imbibiton is the process of wetting phase fluid within the pore space spontaneously exhausting and driving the non-wetting phase,which is one of the important mechanisms for tight reservoirs to improve recovery.Due to the complexity of porous media characteristics and fracture morphology and other factors,the researches on imbibiton and mass transfer laws between fractures and pores have not yet been fully elucidated.In this paper,based on the phase field method and fluid motion equations,a pore-scale dynamic imbibiton and suction numerical model was established to analyze the mass transfer mechanism between fractures and pores within complex pore structures and the relationship with the recovery rate.The results show that:(1)the imbibiton process mainly covers three key stages inside the pore space:rapid penetration of the fracture,interaction between the fracture and the pore space,and gradual advancement in the pore space(i.e.,repulsion process).A faster injection rate will hinder the imbibiton process,and result in more residual oil retention.(2)There is a specific critical fracture width,and when the fracture width is about 40 times the average pore size,the recovery rate will fluctuate up and down in a certain range.As the critical fracture width decreases,the positive correlation between the fracture dimensionless number and the recovery rate is shown.(3)Fracture systems of different complexity have different effects on fluid transport.As the critical fracture width decreases,the impact of different fracture complexity on fluid mobilization is different.Specifically,with the increase of fracture complexity,the wave range of imbibiton effect become larger.The decrease of crack width will exacerbate the phenomenon of oil droplet aggregation,which will significantly slow down the recovery rate and cause clogging problems in the small pore area.(4)The number increase of the system open boundaries can effectively enhance the contact area of the wetting phase,which can maximize the dynamic utilization of the pore space,and form a synergistic seepage drive mechanism.The optimal imbibiton recovery was achieved under the four-sided open(AFO)condition,while the worst recovery was achieved under the one-sided open(OEO)condition.At the same dimensionless time,TEO and OEO show higher normalized recovery rates due to the strong non-homogeneous effect of the open number of end faces and spatial distribution model,while the recovery change curves of the remaining three boundary conditions show relatively concentrated trends.

Low salinity water flooding is a new technology for enhancing oil recovery by adjusting the ion composition or con-centration of injected water.However,the applicable reservoir conditions and enhanced oil recovery mechanism of low salinity water flooding have not yet reached a consensus.In this paper,a series of laboratory experiments of wettability control-based low salinity flooding are carried out with plunger rock samples from marine carbonate reservoirs in the Middle East as the research object.Based on the theory of Derjaguin-Landau-Verwey-Overbeek theory(DLVO),an interfacial reaction model of a typical crude oil/brine/rock system is established,and the contact angle and total separation pressure are calculated simultaneously with the augmented Young-Laplace formula.The reliability of the model is verified by the literature experimental data,and the effects of ion concentration and ion type on the separation pressure curve and contact angle are clarified.The results show that in low salinity environments,the pore surface of carbonate rock is more water-wet under the action of fluid flushing,the oil displacement efficiency is higher,and the low salinity water improves the crude oil recovery by 3.2%;under the assumption of constant charge,the mathematical model established based on the DLVO theory for the crude oil/brine/rock system can accurately predict the change of contact angle;compared with the ion concentration,ion type has a greater impact on separation pressure and contact angle.Among divalent ions,Mg2+ions exhibit a more pronounced influence on wettability control compared to Ca2+ions.When the water film thickness is minimal,van der Waals force is the main force affecting the separation pressure.As the thickness of water film increases,the electric double layer force gradually becomes the main force.This study contributes to a deeper understanding of the wettability control mechanism of low salinity water flooding for enhanced oil recovery.

Total organic carbon(TOC)content is a crucial geochemical parameter for assessing reservoir quality and hydro-carbon generation potential of source rocks.The accurate prediction of TOC content is important for optimizing the exploration and development processes of shale oil and gas.With the rapid development of artificial intelligence technologies,individual machine learning algorithms have been increasingly applied to evaluate TOC content in shale.Despite the promising results of the individual machine learning algorithms,they are often subject to several challenges including overfitting,underfitting,and getting trapped in local optima of objective function.To address these limitations,the ensemble learning models are developed.Ensemble learning models leverage the strengths of multiple individual intelligent algorithms to enhance prediction accuracy and stability.Among them,combination strategy is one of the key factors in optimizing the ensemble learning models.Arithmetic average method as the simplest combination strategy fails to fully use prediction performance of the best individual intelligent model,and it can be severely affected by the individual intelligent model with a large prediction error,which can interfere with prediction outcome of overall model.In comparison,weighted summation method as a common combination strategy assigns the weights to different individual intelligent models according to their performance on training data.This method will perform excellently on training set,but it tends to have a poor performance when applied to test set.This paper develops an ensemble model based on an intelligent matching technology(IMTEM).The proposed method utilizes a set of robust intelligent algorithms including extreme gradient boosting,random forest,support vector machine,and extreme learning machine as algorithm modules to initially process input data.Then,the processed feature information combined with original log responses is fed to feedforward neural network layer for nonlinear transformation and feature learning,thereby enabling accurate and continuous estimation of TOC content in shale.To validate effectiveness of the IMTEM,the proposed method is applied to the prediction of TOC content in the Longmaxi Formation shale in the Sichuan Basin.Test results indicate that,compared to two ensemble models,five baseline models,and the ΔlogR method,predictions of the IMTEM exhibit higher consistency with measured TOC content.This demonstrates that the IMTEM is more suitable for predicting TOC content in shale.

Deep and ultra-deep oil and gas resources, characterized by vast potential but low proven rates, become a key target of exploration and development in China presently. However, evaluating their resource potential still faces a series of scientific and technological challenges, such as high thermal evolution degree of source rocks, strong diagenetic modification of reservoirs, multi-stage adjustment, transformation and effective preservation of oil and gas reservoirs. Recently, new breakthroughs have been made in ultra-deep exploration in the Yuanba Area, with the discovery of natural gas reservoirs in the fourth section of the Dengying Formation at a depth of nearly 9000 meters, revealing promising exploration prospects for ultra-deep layers in the northern Sichuan Basin. Based primarily on the latest drilling data of YS1 well, combined with peripheral drilling, outcrop and analysis testing data, this study systematically investigates the key control elements of source rocks, reservoirs and oil and gas accumulation processes in the Dengying gas reservoir in the study area, aiming to provide reference for the exploration and evaluation of ultra-deep oil and gas reservoirs. The results show that: (1) The YS1 gas reservoirs of the fourth member of the Dengying Formation were derived from the Cambrian Qiongzhusi Formation source rocks. These source rocks entered a low maturity stage during the Silurian, then reached a medium high maturity stage for the main oil generation and early cracking during the Late Permian-Triassic, and reached a high over maturity stage for main cracking gas generation during the Middle Jurassic-Early Cretaceous. (2) The YS1 gas reservoirs are consist of the microbial dolomites deposited on the platform margin, which have undergone long-term compaction, pressure solution, and deep burial cementation, resulting in currently low porosity and low permeability characteristics. (3) In northern Sichuan Basin, the platform marginal mound-shoal reservoirs are adjacent to the high-quality deep-water facies source rocks of the Qiongzhusi Formation, and has favorable source and reservoir configuration conditions of “source generation in slope facies with reservoir accumulation in margin facies” and “upper source feeding lower reservoir", which provides the material basis for paleo-oil reservoir formation. (4) The gas reservoir in Member 4 of the Dengying Formation underwent multistage modifications. During the paleo-oil stage, located on the central Sichuan paleo-uplift slope, it formed large-scale lithologic paleo-oil reservoirs sealed by tight inter-shoal layers. During oil-gas conversion and gas reservoir stages, influenced by the Micang Mountain uplift, subtle structural highs developed on the Micang uplift slope, forming structure-lithology composite paleo-gas reservoirs. In the late stage, the Himalayan compression caused basin-margin uplift, adjusting the paleo-gas reservoir to form current reservoirs, with YS1 well in the favorable overlap zone. Exploration should target large paleo-oil reservoirs, identify key-period paleo-structures, and focus on areas combining effective preservation with paleo-present structural overlap as preferential enrichment zones.

Unconventional oil and gas,represented by shale oil and gas and low-permeability tight oil and gas,are important replace-ment resources in China.Due to their poor reservoir properties,effective exploitation is difficult.Currently,reservoir transformation is mainly carried out through horizontal well volume fracturing technology.However,there are still problems such as water resource waste,reservoir damage,and poor increasing production.Because of the the low viscosity,high density,and high diffusivity,super-critical/liquid CO2 can quickly enter reservoir micropores and microfracture.CO2 fracturing can effectively reduce fracture pressure,form complex fracture networks,increase formation energy,improve backflow rate,reduce reservoir damage,and achieve single well production increase.It is suitable for efficient and green exploitation of unconventional oil and gas resources.This paper introduces the basic research progress of CO2 fracturing from the aspects of the mechanism of rock breaking and fracture making by CO2 fracturing,the mechanism of influence of CO2 on rock properties,the flow characteristics of CO2 fracturing wellbore,CO2 fracturing stimulation and geological storage,and briefly describes the development and application of several main fracturing technologies,including CO2 foam fracturing,CO2 dry fracturing,CO2 acid fracturing,and CO2 mixed fracturing.Against the background of China's current"dual carbon"goals and increased exploration and development efforts for unconventional oil and gas resources such as shale oil and gas,CO2 fracturing technology is one of the key core technologies for building a clean,environmentally friendly,low-carbon,and efficient energy security and development technology system,with great potential for development and promotion.

针对具有一定厚度的、整装的特稠油油藏,蒸汽辅助重力泄油(SAGD)相比于其他热采方法,开发效果更好.目前研究认为SAGD主要通过重力机理开采稠油而忽略了注采压差对SAGD开发的影响,导致矿场预测误差较大.本文针对这个问题,对SAGD生产过程中的注采压差进行了详细研究,基于加拿大Mackay River和Dover区块地质参数,建立地质模型,研究了注采压差对采油速度、SAGD开发稳产时间、蒸汽腔上升阶段及蒸汽腔横向扩展阶段的影响.结果表明:注采压差在SAGD开发过程中起重要作用,随着注采压差的增大,采油速度呈现先快速增加后增速变缓的趋势;在蒸汽腔上升初期,腔体呈扇形,一段时间后呈近似六边形;蒸汽腔到达油层顶部并不一定出现最大泄油速度,最大泄油速度一般在蒸汽腔到达油层顶部一段时间之后出现;注采压差影响着蒸汽腔上升扩展角的变化,而扩展角决定着蒸汽腔上升时的波及范围;注采压差在蒸汽腔上升阶段起着重要作用,而在其横向扩展阶段作用开始减弱.因此在现场实践中,SAGD生产前期可以适当的提高注采压差,而在蒸汽腔横向扩展阶段适当的减少注采压差,这样可以降低发生汽窜的概率,从而达到最优经济效益.

In the process of fracturing in tight reservoirs,the imbibition and displacement of crude oil in reservoir pores by fracturing fluids has gradually become a key research field of enhanced oil recovery technology.However,the production characteristics and mechanism of pore crude oil at different scales in the process of imbibition are still unclear,which seriously restricts the optimal design of fracturing fluid system and the reasonable selection of mining technology.Taking the Chang 7 member tight reservoir in the Ordos Basin as the research object,the amphoteric surfactant(EAB-40)was used as the main agent of the clean fracturing fluid system,combined with T1-T2 two-dimensional nuclear magnetic resonance and wettability test,the influence of surfactant concentration on reservoir interface properties and fracturing fluid imbibition and displacement efficiency was systematically studied,and its microscopic mechanism was revealed.The experimental results show that EAB-40 signifi-cantly enhances the capillary driving force and crude oil desorption efficiency by synergistically reducing the oil-water interfacial tension(up to the order of 10-2 mN/m)and inducing the wettability reversal(the contact angle is reduced from 147° to 57.34°).The comprehensive oil displacement effect of the fracturing fluid system is optimal when the concentration of surfactant is 0.1 wt%.During the imbibibibition process,the wettability inversion is caused by the concentration of water-wet minerals in the small pores,and the diffusion of surfactants causes the wetting inversion,which drives the crude oil to migrate efficiently from the small pores T2<1 ms to the middle(T2 is between 1 and 100 ms)and large pores T2>100 ms.Polymer molecules improve the rheological properties of the fracturing fluid system and promote the deep utilization of residual oil in bound oil and blind end pores.Realize the triple synergistic imbibibibibition mechanism of"IFT reduction-wetting inversion-viscoelastic flow control".

Multiphase flow in porous media is an important research topic in the field of oil and gas reservoir development.Due to the complex geological conditions in China,properties of rocks,such as permeability and porosity,are complex and hetero-geneous.The numerical solution for the complex multiphase flow problems needs to overcome challenges such as the system's multiple variables,strong nonlinearity,large computational cost,and the preservation of physical properties.For the traditional incompressible and immiscible two-phase flow model,the IMplicit Pressure Explicit Saturation(IMPES)semi-implicit scheme is a widely-used important algorithm for solving such problems,where the pressure equation is solved implicitly,and the saturation is updated explicitly.However,the traditional IMPES scheme requires the calculation of saturation gradients when updating the saturation.Therefore,it is not suitable for solving the two-phase flow problems in complex heterogeneous media.Hoteit and Firoozabadi proposed an improved IMPES method,allowing the method to reproduce discontinuous saturation in heterogeneous media.However,these two IMPES methods only update the saturation through the mass conservation equation of one phase of fluid,they cannot guarantee that the other phase of fluid also satisfies the local mass conservation property.The derivations of the pressure equations for these two IMPES methods are obtained by adding the volume conservation equations of each phase at the continuous level of partial differential equations,and then using incompletely matched spatial discretization methods for the pressure equation and the saturation equation.Therefore,it is impossible to simultaneously ensure the local mass conservation of each phase for the two-phase fluid.In this paper,based on several types of novel IMPES semi-implicit schemes for solving two-phase flow in porous media that we have published in recent years,we propose a new framework for deriving the pressure equation in IMPES.That is,we first discretize the volume conservation equation of each phase using a spatial discretization method with local conservation,and then add up the discretized volume conservation equations of each phase.In this way,a complete match in spatial discretization between the pressure equation and the saturation equation is achieved.Essentially,it overcomes the difficulty in previous literatures that the IMPES semi-implicit method cannot simultaneously ensure that both phases of the fluid satisfy local mass conservation.The novel IMPES method ensures that each phase of the fluid satisfies local mass conservation,the saturation is bounded,the computational scheme is an unbiased solution,and it is suitable for solving two-phase flow problem with different capillary pressure distributions in heterogeneous porous media.The novel phase-wise conservation IMPES framework proposed in this paper also has an advantage that the traditional IMPES does not have.That is,in the novel phase-by-phase conservation IMPES framework,it is only necessary to define the spatial discretization method of the volume conservation or mass conservation equation,and there is no need to separately define the spatial discretization method of the pressure equation.The solutions of several types of novel IMPES semi-implicit schemes that we have published in recent years can be regarded as special cases of the novel phase-by-phase conservation IMPES framework proposed in this paper.The IMPES framework in this paper can also be applied for more complex multi-component and multi-phase flow in porous media to construct more novel schemes.At the same time,through numerical examples of heterogeneous porous media,this paper verifies the effectiveness and superiority of the novel IMPES method in dealing with two-phase flow problems under complex geological conditions.Compared with the traditional method,it is more adaptable,more stable,and more efficient.

Shale oil is one of the most potential and strategic alternative oil resource in China.It's of great significance to clarify the fluid distribution and evolution laws in porous media for enhancing the recovery of shale oil during the fracturing-soak-ing-producing process.In this work,a multi-component multiphase lattice Boltzmann model was adopted to study the shale oil flow mechanism during fracturing-soaking-producing process.Firstly,the accuracy of the model was verified using Laplace's law,contact angle,and stratified flow.Then,based on the scanning electron microscope(SEM)image of Jiyang shale,the struc-ture of the shale porous medium was constructed including the distribution of fracture and matrix pores.Subsequently,the lattice Boltzmann model was used to simulate the fracturing-soaking-producing process of shale porous media,and the fluid distribution characteristics at different stages were analyzed.Then the effects of different soaking time,reservoir wettability and drainage rate were explored further.The results show that the fracturing fluid will seep into the matrix pore and replace the oil phase under the action of capillary force during the soaking stage,and with the increase of soaking time,the backflow rate of fracturing fluid return tends to decrease;the water-wet core has a better development effect than the neutral and oil-wet cores,and the utilization rate of fracturing fluid and the degree of crude oil utilization in the matrix are higher;the higher drainage rate will make the pore pressure drop rapidly,which is not conducive to the development and production of the shale oil.The fluid flow mechanisms during the shale oil fracturing-soaking-producing process are investigated from a pore-scale perspective,which provides support for the formulation of a reasonable production schedule for shale oil wells.

The progressive application of artificial intelligence technology within oil and gas exploration has resulted in an inevitable shift towards the transformation of geomechanical parameter prediction from a traditional to an intelligent approach.This paper presents a comprehensive review and critical analysis of machine learning algorithms in the direct and indirect pre-diction of rock mechanics parameters,pre-drilling prediction,monitoring while drilling and post-drilling evaluation of formation pore pressure,1D in-situ stresses and 3D in-situ stresses field prediction.Furthermore,the paper compared machine learning models,input parameters,sample data volume,output parameters,and model prediction performance under different tasks.It has been demonstrated that machine learning algorithms exhibit superior performance in terms of accuracy,timeliness,and applicability in geomechanical parameter prediction compared to laboratory tests,field tests,and empirical model calculations.The current research emphasis is on hybrid models,deep learning models,and physical-constrained neural network models,which have been validated as highly accurate,robust,capable of generalization,and easily interpretable.However,the existing research primarily concerns the prediction of 1D geomechanical parameters post-drilling.Consequently,it is not possible to effectively predict 3D geomechanical parameters prior to drilling or during the drilling process.In order to facilitate the digital and intelligent transformation of geomechanical parameters,an intelligent prediction framework for geomechanical parameters is proposed in this paper.This framework considers the influence of multi-source data,including seismic,logging,and mud log data on the prediction of geomechanical parameters.The machine learning model,which is driven by data and physics,enables the prediction of 3D geomechanical parameters.This model is updated in real-time through the most recent drilling data,thus allowing for the pre-drilling prediction,monitoring while drilling and post-drilling evaluation of regional 3D geomechanical parameters.In addition,the key technical problems facing the intelligent prediction of geomechanical parameters are identified:(1)The transformation of unstructured data types should be minimized,the complexity of the data set should be reduced,and the consistency and comparability of the data should be ensured.(2)Multi-source data fusion should be conducted,and multi-source data sets,including seismic,logging,mud log,laboratory tests,and field test data,should be constructed.Subsequently,data processing and feature selection should be performed.(3)Machine learning models should be enhanced to improve performance,integrated models should be adopted to improve prediction accuracy,and mechanism models and domain knowledge should be integrated to enhance model robustness and explainability.

According to the low porosity,ultra-low permeability and neutral partial oil wetting of shale reservoir,the corre-sponding microscopic model of capillary bundle is designed.The wettability of microscopic model changed by the compound system of molecular film agent(DM)and surfactant octadecyl trimethyl ammonium chloride(STAC)was studied.It is found that DM(1000mg/L)/STAC(concentration≤critical micelle concentration),the wetting modified contact angle is positively correlated with the STAC concentration,the maximum contact angle can reach 100.51°,and it is a monolayer adsorption with an average adsorption thickness of 2.064nm;Dm(1000mg/L)/STAC(concentration＞critical micelle concentration),the wetting modified contact angle is negatively correlated with the STAC concentration,and the adsorption layer is multilayer adsorption.Taking shale oil reservoir of Permian Lucaogou Formation in Jimusar sag as a feature,a capillary bundle model equivalent to pore throat diameter was etched,with radius of 5μm and depth of flow channel of 5μm.Then,through DM/STAC wetting modification,based on hydrophilic wetting and wetting modified capillary tube bundle model,the differential pressure-flow method was used to test the fluid percolation law.As a result,when the fluid flows at low speed,it is characterized by non-Darcy percolation and has a threshold pressure gradient.Moreover,the change of wettability causes the capillary force to turn,affecting the law of fluid percolation.

Shale pores serve as the primary reservoir space for shale gas,whose structural characteristics directly determine the gas occurrence state,enrichment degree,and flow mechanisms.However,the complex structure and strong heterogeneity of organic pores in shale gas reservoirs significantly constrain precise reservoir evaluation and dynamic development.To clarify the three-dimensional structural characteristics of organic pores in the Lower Paleozoic shale reservoirs in South China,this study focuses on two organic-rich shale successions in the northern Guizhou:The Lower Cambrian Niutitang Formation and the Lower Silurian Longmaxi Formation shales,which exhibit significantly different thermal maturities.An integrated approach was employed,combining organic matter extraction,low-temperature nitrogen adsorption,and focused ion beam-scanning electron microscopy(FIB-SEM)three-dimensional reconstruction techniques to systematically characterize the microstructure of organic pores in these two shale successions.Based on nitrogen adsorption and FIB data,the Frenkel-Halsey-Hill(FHH)and box-counting models were respectively applied to evaluate the complexity of organic matter pore structures across different scales.The results show that the moderately mature Longmaxi Formation shale(equivalent vitrinite reflectance Ro=2.1%～2.8%)contains well-developed organic pores,predominantly exhibiting bubble-like and sponge-like cluster morphologies with pore sizes(r)mainly ranging from 200 nm to 450 nm,along with high specific surface area(133.9～159.5 m2/g)and substantial pore volume.In contrast,the overmature Niutitang Formation shale(Ro=3.0%～3.8%)contains smaller organic pores(r=10～140?nm)with irregular or slit-shaped geometries,showing lower specific surface area(30.9～31.4 m2/g)and reduced pore volume.Three-dimensional pore network modeling further reveals distinct connectivity patterns between these two shale successions.In the Longmaxi Formation shale,organic pores are primarily isolated with poor connectivity,and large pores(r>140 nm)contribute approximately 70%of the total pore volume.The Niutitang Formation shale,however,shows enhanced connectivity among large pores(r>150 nm)through thermal-induced microfractures formed during organic matter condensation,while small pores(r<150 nm)remain largely isolated yet account for 64%of the total pore volume.Fractal dimension analysis highlights additional structural differences.The Niutitang Formation shale exhibits higher fractal dimensions for large organic matter pores(D2=2.37～2.78),indicating greater structural complexity,whereas the organic pores of the Longmaxi Formation shale display relatively regular geometries with lower fractal dimensions.These variations are mainly controlled by differences in thermal maturity.Our study provides systematic understanding of three-dimensional pore structure evolution in shales with different thermal maturities,and offers theoretical foundations for shale gas reservoir evaluation and development strategies in northern Guizhou.

Random noise and coherent noise interfere with seismic data collected in the field,resulting in the reduction of the signal-to-noise ratio,which affects the subsequent processing of seismic data,such as seismic migration and imaging.There-fore,it is necessary to develop an efficient and adaptive method to attenuate random and coherent noise in real seismic data.Conventional supervised learning algorithms need to manually generate a large number of labels to train the network,which is very difficult in the field of seismic exploration where the data volume is small.In addition,supervised learning-based noise attenuation methods are expensive in terms of computation and labor costs.To solve this problem,this paper constructs an adaptive deep learning framework based on unsupervised learning strategies to attenuate random and irregular(erratic)noise in multi-dimensional seismic data.This method uses the corresponding structure of encoding and decoding to compress and reconstruct data features.In order to improve the network's attention to important waveform features,this paper uses a soft attention mechanism to assign more weight to important waveform features in a weighted way.In this paper,the multi-di-mensional noisy data is segmented into a large number of one-dimensional noisy signals,which are input into the network for iteration,so as to adaptively attenuate random and erratic noise in seismic data.This small-scale signal denoising method can effectively improve the noise attenuation performance of the network and help to avoid artifacts.In this paper,we use a more robust Huber loss function to attenuate random and erratic noise,which combines the root-mean-square error with l2 norm and the average absolute error loss with l1 norm.In addition,a Total Variation(TV)regularization term is added to the constructed network to capture the local smooth structure of the seismic data.By adjusting the weight of Huber loss function and TV regularization term,the network can obtain the best denoising performance.The method constructed in this paper can be directly used for attenuation of random and erratic noise of multi-dimensional seismic data,and ensure transverse continuity of seismic signals after reconstruction.We compare the proposed framework with classical seismic data denoising methods and noise attenuation methods based on unsupervised learning to analyze the advantages and disadvantages of each method.The test results of 2D and 3D synthetic data and actual seismic data show that the proposed method has better noise attenuation and useful signal protection capabilities.

深度学习善于从原始数据输入中挖掘其内在的抽象特征,十余年来,其在语音识别、语义分析、图像分析等领域取得了巨大成功,也大大推动了人工智能的发展.本文基于深度学习中广泛应用的卷积神经网络算法,以大庆油田某区块密井网数据为对象,开展自动地层对比试验.实验中,随机选取部分井作为训练样本,对另一部分井分层进行预测,并与原始分层数据比对进行误差分析.按照训练样本的井数据比例65％、40％、20％和10％,将实验分为4组,每组实验包括油层组、砂层组和小层级3个相互独立的实验.12个实验结果表明:训练量越大,地层级别越高(厚度越厚),自动对比效果越好;20％的训练量就可以较可靠地进行砂组及以上级别地层单元(厚度不小于10 m)的自动对比.该实验表明卷积神经网络算法能有效应用于依据测井曲线进行油藏规模地层自动对比,具有良好的发展前景.

塔里木盆地经过50多年勘探,证明该盆地油气资源十分丰富,其中,石油资源量120.65×108 t,天然气14.7×1012 m3.目前共发现油气田30多个,其中大型油气田12个,累计探明油气地质储量约23×108 t,天然气储量2×108 m3,油气产量年产3400×104 t油当量,塔里木盆地已成为我国第一大天然气区和油气储产量快速增长的地区.本文论述了塔里木盆地近期油气勘探重大进展、油气分布规律、油气资源潜力及下步勘探方向,对当前和今后油气勘探和科学研究有重要指导作用.

Drillstring dynamics is the basis of safe and efficient drilling,and many researchers have made in-depth research on it with fruitful results.However,the safety of drill string is faced with severe challenges due to its extremely large slenderness ratio,harsh working environment and significant nonlinear characteristics,especially in extra-deep wells exceeding 10,000 meters.Therefore,it is essential to grasp the research process of drill string dynamics and extract the core key issues.Based on a brief review of the research progress of the three basic drill string vibration forms,this paper focused on the research progress of the coupled vibration of these three basic vibration forms,and discussed in detail the harm,formation mechanism,research methods,measurement techniques and control methods of two complex vibrations:stick-slip and whirl.In particular,the research of high frequency torsional oscillation(HFTO),which has attracted much attention in recent ten years,was summarized.Finally,preliminary conclusions and prospects were provided,aiming to offer useful references for the innovative development of drill string dynamics mechanisms and control methods.The existing research results showed that the whirl is the most complex vibra-tion form of downhole drill string,including forward and backward regular whirl and irregular whirl motion,and most of them are irregular whirl motion.Among all the whirl modes,backward regular whirl is more harmful to the drill string,which will not only increase the fatigue damage of the drill tool,but also reduce the mechanical penetration rate of the bit.Stick-slip vibration is another highly harmful and complex form of vibration,which is induced by the interaction between the drill bit and the rock and can be well described by the velocity weakening effect.Stick-slip vibration is closely related to whirl motion.The maximum rotational speed in the slip stage of stick-slip vibration may reach 6-8 times of the ground rotational speed,accompanied with violent whirl motion.The high frequency torsional oscillation,whose vibration frequency is much higher than that of eddy and stick-slip vibration,is even more harmful,and the related mechanism is far from clear and is urgent to explore.

陆相页岩油是一个全新领域,近期的勘探实践已在鄂尔多斯盆地、松辽盆地、渤海湾盆地、准噶尔盆地、柴达木盆地等不同页岩层系取得了重要进展,截止2022年底,页岩油探明、控制、预测三级地质储量达44亿t,2022年产量达318万t.页岩油勘探理论和技术也取得了一系列进展,创新了有机母质类型分析与有机质生排烃实验、储层表征技术、页岩油赋存状态与含油性分析、保压取心与现场测试等页岩实验测试分析技术,基本上能满足页岩油相关实验测试要求;在细粒沉积与有机质富集机理、陆相页岩纹层结构与组合类型、储层孔缝结构与储集性、页岩油富集机理取得了一系列新的认识,指导了重点地区选区选带评价研究.研发了烃源岩品质测井评价、储层品质测井评价、工程品质测井评价、岩石物理敏感参数分析和定量预测、多任务学习储层参数预测、各向异性地应力预测、水平井地震地质导向评价、富集层(甜点)综合评价等技术,并推广应用,在页岩油储量提交、甜点区优选、水平井部署、随钻导向预警和钻完井工程改造方面提供了重要且面向全周期的技术支撑.但陆相页岩油规模勘探与效益开发目前还面临诸多挑战,必须建立全新的研究内容与研究重点,特别需要把研究精度升级并要加强微观研究,加强固/液/气多相多场耦合流动机理研究,加强多学科交叉研究等,以建立页岩油成藏新学科.

Reservoir physical parameters serve as fundamental quantitative indices for characterizing the storage capacity and fluid percolation potential of subsurface reservoirs.Well logging interpretation,a critical methodology for accurately estimating these parameters,constitutes a sophisticated nonlinear regression challenge.To address the inherent limitations of existing petrophysical parameter interpretation techniques,particularly their inadequate generalization performance under few-shot learning conditions,this investigation systematically devises a dual-framework analytical approach.This study initially proposes a sample optimization methodology based on cluster analysis.The spatial configuration of samples is partitioned through the implementation of the K-means clustering algorithm,followed by selective sample curation according to spatial distribution char-acteristics to maximize learning sample diversity.Building upon this optimized sample architecture,the study further introduces a hierarchical residual neural network-based interpretation framework for petrophysical parameter estimation.The proposed methodology enhances conventional fully connected neural architecture through four innovative mechanisms:(1)Integration of cross-layer residual connections facilitates progressive refinement of residual mappings between multivariate logging inputs and target petrophysical outputs,thereby enabling hierarchical abstraction of complex petrophysical relationships from limited training instances.(2)The integration of ensemble learning paradigms amalgamates diverse machine learning methodologies,effectively mitigating overfitting risks through algorithmic diversity.(3)The implementation of a multi-task learning framework establishes intrinsic correlations between porosity and permeability interpretation tasks via shared latent representations,thereby enhancing individual task generalizability under data scarcity constraints.(4)The introduction of a quadratically weighted root mean square error loss function preferentially reduces interpretation errors in high-permeability reservoir intervals.Results from 90 rigorously designed comparative experimental configurations in the study area demonstrate that the cluster-based sample opti-mization methodology effectively enhances generalization performance across multiple machine learning models under few-shot learning constraints.Application of the proposed hierarchical residual neural network framework for well-logging interpretation of reservoir porosity and permeability within the investigated reservoir area achieves coefficients of determination of 88%and 94%,respectively,demonstrating statistically significant superiority over conventional methodologies in both petrophysical interpretation accuracy and generalization capability.Blind testing validation on cored wells reveals 12 and 20 percentage point improvements in predictive precision compared to other various existing methodologies,the proposed approach in this study demonstrates substantial advancements in addressing few-shot learning challenges through algorithm optimization strategies encompassing distribution-based sample selection and multi-task collaborative frameworks.This methodology significantly enhances feature representation fidelity in petrophysical datasets,exhibiting superior petrophysical interpretation accuracy and enhanced generalization capabilities.

In response to issues such as high water cuts and simultaneous gas-water production during the development of the J58 well block in the Ordos Basin,this study evaluates the influence of various reservoir factors on movable fluids based on pore-throat size classification in tight sandstone reservoirs.This helps to clarify the gas distribution pattern from a microscopic perspective.Taking 10 typical tight sandstone cores from the Shihezi Formation as examples,casting thin section observation,scanning electron microscopy(SEM),X-ray diffraction(XRD),high-pressure mercury intrusion(HPMI),and nuclear magnetic resonance(NMR)experiments were conducted.Using multifractal theory and NMR parameter-based pore-throat distribution transformation methods,the impact of reservoir parameters on the distribution of movable fluids within pore throats of different sizes was assessed.The results show that based on the shape and parameters of mercury intrusion curves,the pore structure can be divided into three types.Type Ⅰ shows a bimodal distribution of pore-throat sizes,with good physical properties and connec-tivity;Type Ⅱ shows an unimodal distribution dominated by medium-sized pores,with good sorting,but due to limited pore-throat size,their physical properties are inferior to Type Ⅰ;Type Ⅲ have a pore-throat size distribution dominated by nanopores as the main peak and mesopores as the secondary peak,with the strongest heterogeneity in physical properties.According to the turning points in pore-throat size and fractal characteristic curves,the pore throats can be classified into mesopores(0.1～1 μm),micropores(0.01～0.1 μm),and nanopores(0.001～0.01 μm).Movable fluids are mainly found within mesopores and micropores,where the mesopores content plays a decisive role in the volume of movable fluids,while micropores,when in relatively high proportion,also have certain gas storage potential.Nanopores,however,have little impact on movable fluid distribution.The content of brittle minerals mainly affects the amount of movable fluid in mesopores,whereas clay mineral content has a negative impact on movable fluid content across all pore-throat sizes.The porosity contributed by different pore-throat sizes is positively correlated with movable fluid content;however,this correlation decreases as pore-throat size decreases due to the influence of reservoir connectivity.Permeability controls the distribution of movable fluids within pore throats of different sizes.Among pore-throat structure parameters,a higher fractal dimension negatively affects the distribution of movable fluids both overall and within pore-throats of different sizes.Owing to the limitations imposed by differing contributions of pore-throat sizes to reservoir properties,the maximum mercury saturation parameter can only be used to characterize the distribution of movable fluids within mesopores.

目前在天然气管道应急抢修过程中氮气置换工艺的选取主要依据经验进行,具有一定的盲目性.为增强其理论指导性,文章利用Stoner Pipeline Simulator(SPS)软件对川气东送某事故管段建立了动态物理模型,并分析了破损口综合特征(位置及大小)对氮气置换工艺的影响.分析了破损口位置对单端注氮氮气置换工艺总置换时间的影响,结果表明单端注氮时选取远端注氮工艺总置换时间较短.分析了破损口位置、破损口当量直径对单/双端注氮总置换时间的影响,结果表明破损口当量直径和破损口位置的耦合作用共同决定单/双端注氮工艺的优选结果.单/双端注氮工艺优选存在与破损口状态及破损口位置相关的"转换相图",可通过破损口状态以及破损口位置在"相图"中的位置优选氮气置换工艺,对工程实际具有重要理论指导意义.

To address the challenges of the oil-based drilling fluid system's deteriorating rheological properties and insufficient plugging pressure resistance under high-low temperature cycling conditions in the Yaha gas storage reservoir drilling,a tempera-ture-sensitive high-temperature thickener,RHT,was developed.Optimized plugging materials and supporting agents were selected to construct a high-temperature resistant oil-based drilling fluid system.Characterization methods,including infrared spectroscopy,nuclear magnetic resonance hydrogen spectra,thermogravimetric analysis,and differential scanning calorimetry(DSC),were used to analyze RHT's molecular structure,thermal stability,and temperature-sensitive characteristics in depth.The systematic evaluation of its rheological control in emulsions and oil-based drilling fluids was conducted.Experimental results showed that RHT significantly improved the shear-thinning and thixotropic properties of the emulsion,demonstrating excellent rheological control capabilities under high-low temperature cycling conditions.At 80℃,the dynamic yield stress increased by 87%without any increase in plastic viscosity;at 220℃,the dynamic yield stress increased by 220%,with a dynamic plastic ratio of 0.49 Pa/(mPa·s).The drilling fluid system maintained strong rock-carrying capacity after aging at 220℃and effectively sealed 20～40 mesh sand beds and 1～3 mm cracks,achieving a maximum pressure resistance of 8 MPa.In the field application of the Yaha gas storage reservoir well X,this system significantly enhanced the rock-carrying and plugging performance of the drilling fluid,reducing complexities such as fluid loss and stuck pipe incidents,thereby providing strong technical support for the efficient development of the Yaha gas storage reservoir.

Karst-related carbonate fracture-cavity reservoirs play a vital role in global oil and gas field development.Especially under deep to ultra-deep conditions,their internal structures and filling-modification processes exhibit extreme complexity.Identifying the types and degree of fillings in paleokarst caves carries significant theoretical and practical value for evaluating effective reservoir space,optimizing development strategies,and tapping remaining oil potential.Based on an extensive review of the literature,this study proposes a systematic classification scheme for the filling phases and detrital filling phases of karst caves,highlighting key advancements in the geological understanding of internal cave filling structures.The article summarizes the current models of karst cave filling in the Tahe Area,focusing on technological progress in identifying and predicting filling materials and determining the degree of filling in paleokarst caves.Progress in identifying cave filling facies is primarily reflected in the genetic classification of modern surface cave detrital filling facies and the categorization of paleokarst cave fillings.Early methods for identifying and predicting cave filling materials and assessing filling degrees relied on qualitative and semi-quantitative approaches using logging and seismic data.With the advent of artificial intelligence(AI)technology,the application of machine learning's powerful generalization capabilities to identify and predict filling materials and degrees has emerged as a cutting-edge research direction in this field.The classification of filling modes in paleokarst caves suggests utilizing the coupling relationship between hydrogeology and cave development within the hierarchical structure framework of the paleokarst fracture-cave system.This approach,combined with the types of internal filling materials revealed by actual drilling data,facilitates the construction of filling models.However,current classifications of filling types in paleokarst caves primarily focus on differences in rock physical components,without adequately reflecting the dynamic mechanisms of filling formation.Additionally,the accuracy of identifying cave fillings remains insufficient,hindering the comprehensive determination of the sequence of fillings within caves.Currently,seismic inversion technology,commonly used for predicting cave fillings,can only estimate mud content and fails to accurately evaluate the degree of filling for all materials.Consequently,predicting the spatial distribution of filling degrees in paleokarst underground river networks requires further research and development.In light of these challenges,this article argues that leveraging AI technology to identify and predict the types and degrees of cave filling materials represents a promising trend.Future research should focus on improving the representativeness of sample sets,as well as the accuracy and generalization capabilities of prediction networks.

During the development of CO2 injection in low-permeability reservoirs,carbonated water formed after CO2 dissolves in water can effectively improve the imbibition effect,and thus improve the reservoir development benefit.By measuring the oil-water interfacial tension,contact angle and imbibition recovery factor,the effect of temperature and pressure on imbibition recovery in low-permeability cores under high-pressure CO2 was investigated.The results show that increasing temperature and CO2 pressure can improve oil-water interface characteristics and enhance imbibition recovery.At 8 MPa,the temperature increas-es from 20℃to 80℃,the interfacial tension increases by 2.25 mN·m-1,and the contact angle decreases by 15.2°.The influence of temperature on oil-water interface characteristics is stronger than that of CO2 solubility.With the increase of temperature,CO2 solubility decreases,but the interfacial tension increases,the hydrophilicity of rock enhances,and the fluidity of crude oil increases,so the imbibition efficiency increases.At 80℃,the pressure increases from 4 MPa to 10 MPa,the interfacial tension decreases by 3 mN·m-1,and the contact angle decreases by 18.4°.Pressure mainly affects the oil-water interface characteristics by changing the CO2 solubility in the liquid phase.With the increase of pressure,the CO2 solubility increases,the interfacial tension decreases,the hydrophilicity of rock enhances,the fluidity of crude oil also increases,so the imbibition efficiency increases effec-tively.Heating and pressurization have a certain synergistic effect on improving imbibition efficiency.Under the combined action of the two,although the interfacial tension only slightly decreases,the hydrophilicity of the rock enhances significantly,which accelerates the escape of crude oil in the matrix pore throat and effectively improves the imbibition recovery in low-permeability cores.The research results enrich the imbibition production mechanism,and can provide theoretical reference for CO2 injection development in low-permeability reservoirs.

The lower Cambrian organic-rich shales are widely distributed in the middle-upper Yangtze platform,which are also the main target layers for shale gas exploration and extraction.Revealing the enrichment mechanisms of organic matter in these shales is of great significance for understanding the relationship between ocean bio-environmental systems and Earth resources.Through systematical geochemical analyses,this study investigated the lower Cambrian Niutitang Formation slope facies shales at the western Xuefeng Uplift,to elucidate the paleoenvironmental characteristics and their main controlling factors.The results show that the Niutitang Formation shales generally have high TOC contents,with higher TOC contents in the lower part than those in the upper part.Sedimentological geochemistry analyses indicate that the Niutitang Formation formed under high pa-leo-productivity and anoxic-anaerobic even sulfurized conditions.The formation generally has a lower degree of reduction in the late period of sedimentation than at earlier times,and was deposited in a moderately hydrologically restricted setting,with weak terrestrial input and hydrothermal activities.The enrichment of organic matter in the Niutitang Formation slope facies shales was mainly controlled by the redox conditions,however upwelling currents also contributed to the primary input of organic matter.During the early deposition stage of the Niutitang Formation,the nutrients brought by upwelling currents promoted primary paleo-productivity,resulting in the booming of algal organisms and subsequent bacterial sulfate reduction(BSR)and anoxic even sulfurized environments.During the late deposition stage of the Niutitang Formation,a drop in sea level led to a weaker degree of reduction and lower contents of TOC.Our results are of great significance to the enrichment mechanisms of marine organic-rich shales in petroliferous basins.

The Baima structure belt in the southern part of Fuling shale gas field has experienced many periods of structural movements since the Lower Paleozoic marine shale gasification,and the structural deformation is complicated and different,which has a controlling effect on the gas content of shale gas reservoirs,making the formation mode diverse and restricting the process of exploration and development.This paper analyzes the structural characteristics and evolution process of the Baima structure belt through 3D seismic interpretation,combined with a series of cores and drilling and logging data,and establishes three modes of shale gas enrichment and reservoir formation on the basis of these data.The study shows that:(1)the Baima structure belt mainly develops north-east oriented structures,and exhibits different deformation characteristics of vertical stratification and east-west stratification.Vertically,it is divided into four sets of upper,middle,lower,and Precambrian structural deformation layers by three sets of sliding layers,namely,the Cambrian Qinjiamiao Formation Salt Bed,the Silurian Longmaxi Formation Mud Shale Bed,and the Triassic Jialingjiang Formation Salt Bed.The plane from west to east can be divided into a fold deformation zone and a wide and slow oblique zone.(2)The late Yanshan period is an active period of structural deforma-tion,and the Jiangnan-Xuefeng orogeny produces extrusion from the south-east to the north-west,forming the main north-east oriented tectonic structure of the Baima structure belt.Himalayan period for the structural transformation and finalization of the period,the Indo-European plate collision and the Tibetan Plateau to the east to escape the remote effect caused by the Sichuan Basin counterclockwise rotation,in the eastern part of the Sichuan region for the performance of the right-handed shear,the development of slip shear zones.The Baima structure belt is extruded in the east-west direction,and the first existing northeast structure is modified,and then it gradually turns into the north-south direction.(3)The Baima structure belt can be classified into three shale gas enrichment and storage modes:low-pressure crushed backslope type,normal-pressure wide and slow sloping type,and normal-pressure gentle sloping type.Among them,the shale gas enrichment and preservation conditions in the south slope of Baima are better,with high gas content in a single well.The core and the east wing of the Baima syncline are followed,and the gas content of a single well is higher.The north wing is poor,and the gas content of a single well is slightly lower.The Shimen-Jinping anticline belt is unfavorable for shale gas enrichment and preservation.

This paper presented the development process and applicable conditions of the classical seepage mechanics theo-ry-Darcy's law,and derived the mathematical expressions of Darcy's law in capillary seepage and fracture seepage in porous media from the Navier-Stokes(N-S)equations.The paper pointed out eight major problems in the current application of Darcy's law and comprehensively analyzed the main challenges of seepage mechanics theory in oil and gas field development.In response to these challenges,this paper proposed a series of countermeasures and considerations.The paper emphasized that constructing multi-scale,multi-physics field coupling models and leveraging AI scientific computing is the only way to reveal the complex and real flow mechanisms of oil and gas reservoirs and fill the current theoretical gaps.It was suggested to further develop high-precision experimental techniques such as nuclear magnetic resonance,electron microscopy scanning,and intelligent data and image processing to visually demonstrate the behavior and processes of fluids in reservoirs.Finally,it was suggested to use experimental research,the establishment of new theoretical models and AI for Science to innovate and break through the current challenges in the theory of oil and gas seepage mechanics.It provided an important reference for universities,research institutions and researchers to carry out theoretical research and project establishment of petroleum science,and it can also provided strong technical support for the scientific and technological development strategic planning of oil and gas resources in China.

In order to obtain the actual change characteristics of the gas-water relative permeability in coalbed methane(CBM)reser-voirs and to further deepen the understanding of the gas-water production process of CBM reservoirs,a relative permeability dynamic calculation method of CBM reservoirs combined with the real-time production situation of the reservoir was constructed.Firstly,the production history match of the target single coal seam or multiple coal seams is carried out using the multiple coal seam whole process coupling flow model,which is to obtain the basic physical parameters of the reservoir and fluid.Then,based on the obtained basic physical parameters,the full production cycle productivity prediction of the CBM well is carried out.At the same time,the reservoir parameters are averaged according to the real-time expansion behavior of the reservoir pressure drop,and the gas-water permeability curve of the CBM reservoir is calculated based on the averaged reservoir parameters and the actual fluid migration.The results show that the relative permeability dynamic calculation method of CBM reservoirs proposed in this paper can dynamically calculate the actual gas-water relative permeability of the CBM reservoir on the basis of real-time quantitative analysis of the internal production situation of the CBM reservoir.There is a reversal point on the gas relative permeability curve calculated by the method proposed in this paper.The reversal point characterizes the stable and continuous supply of desorption gas in CBM reservoirs.If the actual expansion of the CBM reservoir pressure drop is not considered and the reservoir parameters are averaged in the whole area,the calculated gas-water relative permeability of the CBM reservoir will be low.For multiple coal seam reservoirs,the interlayer mass exchange of fluids and the decrease in the reservoir pressure caused by the crossflow cannot be ignored.The ratio of the crossflow of gas to gas production can reach 0.57,and the ratio of the crossflow of water to water production can reach 0.69 after 3 years of production.The calculated gas-water relative permeability of the CBM reservoir under the condition of ignoring the crossflow is low,which is only 42.9%and 24.4%respectively of the calculated gas-water relative permeability under the condition of considering the crossflow.In the actual production process of CBM wells,the water saturation remains at a high value,more than 40%.Restricted by the high water saturation,the gas relative permeability of CBM reservoirs is low,less than 0.2.

高分辨率地震数据在地震数据处理中扮演着关键角色,特别是当地震勘探目标变得越来越复杂时,它可以提供更准确的储层识别和描绘.近年来,随着深度学习技术的快速发展,它越来越多地被引入到高分辨率地震数据处理中.基于大量标记数据,建立了低分辨率地震数据和高分辨率地震数据之间的复杂非线性关系.然而,深度学习在高分辨率数据处理中的精度与稳定性高度依赖于训练集的准确性与多样性.深度学习技术在生产中实际应用的主要挑战之一是稀疏的井数据,这经常导致训练集受限.为了解决这个问题,本文提出了一种基于深度学习的高分辨率处理方法,通过使用大量逼真的训练集,将井数据所表示的分层结构与地震数据所表示的空间地质结构相结合.建立训练集包括三个步骤:(1)使用井数据计算波阻抗序列,并利用高斯匹配函数拟合波阻抗高频部分的振幅分布,得到一个概率密度函数,最后生成一系列符合井数据统计分布的波阻抗序列.(2)在波阻抗序列的基础上,建立二维水平分布的波阻抗模型,并逐步添加折叠变形、倾角变形和断层变形,生成包含各种地质模式的二维阻抗模型.(3)使用阻抗模型计算反射系数,然后用反射系数模型分别卷积低频和高频子波,得到训练集.通过自动生成具有地下地质信息的大量训练集,训练的网络可以估计稳定而准确的高分辨率结果.深度学习的框架由两个部分组成:提取输入数据特征的编码部分和通过提取的特征重建输出的解码部分.此外,残差模块被整合到框架中,使网络更有效地从训练集中提取特征进而提高网络性能,从而实现计算精度和效率之间更好的平衡.通过模型数据和实际数据的测试,本文提出的方法相比于传统深度学习方法对噪声具有更好的鲁棒性,可以产生更精确且横向连续性更好的高分辨率结果.

地层孔隙压力是油气井从设计到完钻过程中重要的基础数据,准确计算地层孔隙压力是保障钻井安全、提高钻井效率的重要前提.为了克服传统地层孔隙压力计算方法精度不足、计算效率不高的问题,本文考虑到钻井与地层沉积均为序列性和非线性过程,提出了将长短期记忆神经网络(LSTM)和误差反向传播神经网络(BP)相结合计算地层孔隙压力的方法,利用LSTM层提取钻-测-录多源数据中的序列性特征信息,经过BP层构建特征信息与地层孔隙压力之间的非线性映射关系.通过对油田现场钻测录数据进行清洗并综合相关性分析和钻井经验知识优选了18种输入参数,对LSTM-BP地层孔隙压力计算模型进行训练和测试,并采用网格搜索法对LSTM-BP神经网络模型的5种模型超参数进行了优选,效果最优的单井计算模型和邻井计算模型的平均绝对误差分别为4.92 MPa和2.34 Mpa,均方根误差分别为6.65 Mpa和3.03 Mpa,平均相对误差分别为4.36％和8.31％.最后与传统BP模型、LSTM模型和支持向量机(SVM)模型的最优结果进行对比,结果显示,本文所建立的LSTM-BP神经网络模型精度均高于BP模型、LSTM模型和SVM模型,表明所提出的LSTM-BP地层孔隙压力计算模型具有较高的计算精度.

干热岩地热是一种具有独特优势的清洁可再生能源,具有储量大、分布广、低碳环保、稳定高效等特点,实现干热岩地热资源的高效开发对于推动我国能源结构转型和绿色低碳发展具有重要意义.本文从干热岩地热资源分布和成因类型出发,介绍了我国干热岩地热资源禀赋和热源机制,阐述了干热岩地热的主要开采方式和国内外研究现状,结合已建和在建的增强型地热系统(EGS)示范工程,系统剖析了干热岩EGS开采所面临的钻井建井、压裂造储及取热优化3大挑战,并从基础科学研究层面给出了未来攻关思路:以"岩体表征—钻井建井—压裂造储—流动取热—集成调控"为主体路线,探究干热岩破碎动态力学响应特征与缝网连通机理,阐明热储取热过程中热—流—固—化多场耦合关联机制,形成干热岩钻井建井、压裂造储与取热调控理论及新方法,为干热岩地热资源高效开采奠定理论和方法基础.

传统井壁失稳力学机制研究主要采用弹塑性静力学和孔隙弹性模型,忽略了井眼钻开瞬间引起的孔隙弹性动力学效应,无法解释超低渗透地层井壁围岩破坏的本质.本文基于Biot多孔介质弹性动力学理论,建立了多孔弹性地层各向异性应力场中井壁瞬时失稳力学模型,借助傅里叶变换将二维非均匀地应力场控制方程组分解为两个一维问题,分析了数值求解的收敛条件,模拟了井壁瞬时破坏动态演化过程及多孔弹性无因次参数对瞬时破坏的影响.研究表明,新模型计算出的井周孔隙压力场、有效应力场及切向与径向的有效应力差比经典孔隙弹性力学解大,在最小地应力方位井壁围岩易发生剪切破坏且破坏区域随时间增大,岩石孔隙流体饱和度对井壁瞬时失稳影响最大,饱和流体越多,井壁围岩失稳风险越高.本文所建立的孔隙弹性动力学模型为井壁失稳机理分析、井筒液柱压力设计提供了科学基础,并对超低渗透致密储层井壁失稳控制具有重要的工程意义.

鉴于油井生产过程中流体分布的复杂性和流动变化的随机性,正在兴起的多相流动测量与成像研究遇到严重挑战,尤其是敏感场构建、多模信息融合和流动图像重建等关键技术亟待突破.电磁全息方法旨在根据探测物理场特性,基于成像测量理论,研究建立全新的双模敏感场,对传感器采集信号的实部和虚部信息进行融合,探索高效适用的全息成像方法,并通过模拟流动测量实验检验全息成像可行性和精确度.研究表明,全息测量数据特征区别明显,能够充分突出各相流体导电特性与介电特性的差异;全息测量敏感场相比原有敏感场更加适用于油井多相流动电磁全息方法;基于双模融合的全息成像方法有益于提高全息成像精度.流动模拟测量检验结果表明,油井多相流动电磁全息方法是可行的,具有良好的应用前景.

在微地震监测过程中,压裂区域的应力状态对于水力压裂的效果有着不可忽视的影响,但是由于压裂区域裂缝形态交错,应力场较为复杂,往往很难有效地获取较为准确的应力状态.本文将探究应力场的复杂程度对于应力反演的影响,通过数值模拟均一应力场条件下和复杂应力场条件下的震源机制解,利用Vavry?uk的迭代联合反演方法进行应力反演,并对反演结果进行误差分析.发现均一应力场作用下震源机制的不确定性对于求解应力模型的影响不大,但是会对滑动方向的误差统计带来一定程度的影响;在复杂应力场的条件下进行应力反演,滑动方向的误差值会随着应力场复杂程度的增加而增大,并且比震源机制的不确定性造成的误差值大得多.此外,当应力场的复杂程度增加到一定水平时,反演得到的单一应力模型将使得相当一部分断层面无法满足摩尔—库伦破裂准则,这种反演结果是不可取的.本文还选择了与数值实验有相似特征的两组实际数据,大庆油田某压裂井和长宁区块的某压裂井监测数据,分别进行应力反演和误差分析,用以验证上述结论.

超临界二氧化碳(SC-CO2)压裂改造非常规储层具有提高油气产量、储层无污染、节约水资源、埋存CO2等优点,受到了工业界和学术界的广泛关注.本文综述了目前室内SC-CO2压裂实验方法、裂缝起裂扩展特征与机理,以及存在的问题并给出建议.SC-CO2与岩石的"热(T)—流(H)—力(M)—化(C)"多场耦合作用机理为诱导应力和弱化断裂性质两方面.诱导应力方面包括:SC-CO2低黏度和高扩散性导致的孔隙压力场和热应力场,共同降低有效应力并诱发天然裂缝的剪切破坏(TH-M);SC-CO2相变释放的能量以冲击载荷和热应力的形式促进裂缝动态扩展(TH-M).弱化断裂性质方面包括:零表面张力的SC-CO2可进入微裂纹尖端,降低裂缝扩展所需的缝内净压力(H-M);SC-CO2吸附在微裂纹表面,降低裂缝失稳扩展所需的临界应力(C-M).因此,SC-CO2压裂易于形成以I-II混合型破坏为主的多裂缝,适合改造裂缝性致密储层.未来应积极开展SC-CO2三维裂缝扩展实验模拟,进行裂缝特征重构和定量评价,利用理论模型和数值模拟研究方法,实现研究尺度的升级,加快SC-CO2压裂工业应用进程.