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With the increasing demand for real-time monitoring and efficient management in offshore oilfield development, Distributed Acoustic Sensing (DAS) has emerged as a powerful tool for downhole fluid monitoring and production interpretation. Despite its growing application, the understanding of DAS acoustic response characteristics and underlying mechanisms during well production remains insufficient. Moreover, conventional sound velocity-flow interpretation methods typically require relatively high flow rates, which limits their applicability in domestic wells with lower production rates. Addressing these challenges, this study presents a systematic investigation of the DAS response mechanisms associated with downhole oil-water two-phase flow. A theoretical analysis was first conducted to elucidate the acoustic response mechanisms of multiphase flow within the wellbore. Building on this analysis, a fully coupled physical simulation apparatus was designed, comprising a reservoir analog, a full-scale wellbore, and a test tubular string, replicating realistic downhole production conditions. Experiments were performed to monitor the DAS response during the flow of oil-water mixtures from simulated reservoir cores into the wellbore, considering varying flow rates and water cut conditions. DAS data were processed with advanced denoising techniques, and Frequency Band Energy (FBE) metrics were computed to quantitatively characterize the acoustic response. The experimental results reveal that the viscosity of oil-water mixtures is a key factor influencing DAS responses. Variations in the oil-water ratio generate complex multiscale interfacial dynamic processes and flow regime instabilities, which are identified as the primary causes of changes in the spectral characteristics. The frequency spectrum of two-phase flow within the wellbore was found to be concentrated below 500 Hz. Specifically, when one phase predominates, the spectrum typically exhibits a single dominant peak; whereas for near-equal oil-water ratios, multiple peaks emerge in the frequency spectrum, reflecting the complex dynamics of the multiphase flow. To enable quantitative interpretation, the Reynolds number (Re) was selected as a representative characteristic parameter integrating both fluid properties and pipe geometry. An FBE-Re relationship model was subsequently established, showing a highly correlated logarithmic relationship. This model provides a robust framework to quantitatively link DAS responses to downhole two-phase flow characteristics. Field validation demonstrates that the observed DAS frequency bands closely match the laboratory-determined ranges, and the interpreted total liquid production and water cut achieve an accuracy exceeding 85%. These results confirm the reliability and practical applicability of the proposed FBE-Re model for field production monitoring. Overall, this study significantly advances the understanding of DAS acoustic response characteristics and governing mechanisms in oil-water two-phase downhole flow. The findings provide a theoretical foundation and practical guidance for accurate well production profiling, high water-cut zone identification, and enhanced field-level production monitoring, offering substantial potential for improving offshore oilfield management and operational decision-making.
As a new strategic replacement for shale gas development in China, the Cambrian Qiongzhusi Formation shale gas reservoir in the Southern Sichuan Basin is characterized by great burial depth, high in-situ stress, well-developed natural fractures, and notable inter-well interference. These characteristics pose severe challenges to the evaluation and optimization of hydraulic fracturing performance in new production platforms. Meanwhile, conventional hydraulic-fracturing monitoring methods are limited in spatial continuity, inter-well-scale characterization, and quantitative evaluation of stage-to-stage stimulation differences, making it difficult to continuously monitor complex fracture behaviors such as fracture propagation, fracture-offset-well interaction, and communication with natural weak planes in ultra-deep shale reservoirs. Address these challenges, this study conducted cased-hole offset-well fiber-optic strain monitoring using low-frequency Distributed Acoustic Sensing (DAS) during zipper fracturing operations on a production pad in this area. Strain-rate data were acquired throughout the complete treatment process of 52 fracturing stages from two treatment wells, and the far-field strain-response characteristics associated with hydraulic-fracture propagation were systematically analyzed. A field-data interpretation workflow was established, including raw DAS data quality control, low-frequency information extraction, synchronized analysis of strain-rate waterfall plots and treatment parameters, fracture-arrival-time statistics, shut-in response analysis, and cumulative-strain-based evaluation of reservoir stimulation effectiveness. The results show that the monitoring responses are generally characterized by widespread response, clear depth correspondence, complex response patterns, and significant differences between earlier and later treatment stages. Hydraulic fracture propagation mainly exhibits three dominant modes: competitive propagation of multiple fractures, coexistence of vertical and inclined fractures, and large-scale communication with bedding-parallel fractures. The fracture propagation velocity toward the offset well and the strain response characteristics at shut-in are closely correlated with fracture propagation behavior. The average fracture propagation velocity is jointly controlled by local in-situ stress and natural fracture conditions, the vertical offset between the treatment well and the monitoring well, the degree of communication with previously created fractures, and local stress-field modification induced by zipper fracturing.The strain response during shut-in reveals differences in closure behavior among different fracture types. Fracturing stages dominated by opening-mode vertical fractures commonly exhibit obvious strain reversal after shut-in, indicating relatively sufficient fracture closure. In contrast, intervals affected by bedding-plane communication or shear slip along weak planes generally show slowly decaying tensile strain that is difficult to fully reverse, suggesting that residual fracture opening or shear displacement may remain on the fracture surfaces. This study pioneers the use of hydraulic-fracturing far-field strain monitoring results for quantitative evaluation of reservoir stimulation effectiveness, enabling quantitative assessment of stimulation intensity and the spatial extent of stimulation for different fracturing stages. Furthermore, it documents the monitoring of seismic events during operations and discusses their impact. The study provides a data-driven framework for optimizing fracturing design, mitigating well interference risks, and enabling collaborative development in deep shale reservoirs, while establishing a foundation for future fracture parameter inversion and quantitative evaluation.
Distributed Acoustic Sensing (DAS) technology demonstrates significant engineering application value in monitoring hydraulic fracturing in unconventional reservoirs. This technology enables real-time detection of downhole acoustic events and accurately captures the dynamic variations in acoustic signal intensity and energy, thereby achieving high-resolution, continuous tracking of the entire fracturing process, including plug-and-perf operations, ball-seat activities, and fracturing fluid flow distribution. In offset well monitoring mode, low-frequency DAS signals effectively acquire the strain field distribution induced by hydraulic fracture propagation and its spatiotemporal evolution. Combined with rock mechanics constitutive relationships and fracture propagation models, this approach reveals the intrinsic mechanisms of fracture initiation, propagation, and complex fracture network formation. This paper systematically elaborates on the physical sensing mechanism of DAS, field monitoring applications, and key processing methods for fracturing interpretation. It highlights the fiber-optic monitoring interpretation algorithms developed based on publicly available data from the U.S. HFTS-2 project, as well as the integrated multi-physics fiber-optic monitoring interpretation software platform HiFiber 1.0. This platform establishes a standardized workflow from raw signal processing to engineering parameter inversion, encompassing three core modules: in-well fracturing evaluation, offset-well strain analysis, and production/injection profile interpretation. It enables quantitative inversion of key indicators, including fracturing fluid distribution among perforation clusters, fracture geometric parameters, and fracture complexity, providing a reliable technical means for fracturing design optimization and stimulation effectiveness evaluation.
Accurate in-situ stress determination is vital for evaluating the mechanical state of subsurface rock and supports key petroleum engineering operations such as wellbore stability, hydraulic fracturing design, and fracture propagation control. Increasingly complex geological settings expose the limitations of conventional measurement methods in resolution, accuracy, and continuity. Fiber-optic sensing provides a means for continuous and high-precision monitoring under such conditions. This work presents an in-situ stress measurement method that combines fiber-optic strain sensing with a reverse differential strain mechanism. By applying internal pressure to drive microfractures around the borehole from closure to reopening, the method infers in-situ stress from the mechanical response of t he rock. Laboratory physical simulation experiments were conducted to establish the theoretical framework and measurement workflow. Results show that fiber-optic strain data effectively capture rock anisotropy and heterogeneity, enabling estimation of in-situ stress ratios through the reverse differential strain model. Compared with the Kaiser acoustic emission method, the approach yields an 8%~24% error range, averaging 16%. Although still in its early phase, the method offers a promising technical pathway for in-situ stress determination in oil and gas engineering and provides a foundation for future refinement and application.
Distributed fiber-optic monitoring technology has become an important tool for hydraulic fracturing fracture diagnosis and fracturing performance evaluation. This technology can provide continuous and high-spatial-resolution sensing responses along the wellbore, and therefore provides an effective method for identifying fracture initiation, fracture propagation, fracture communication, fluid distribution, and production contribution. However, due to the diversity of technical schemes and the different monitoring objectives, together with the continuous development of the understanding of fiber-optic monitoring mechanisms and data acquisition, processing, and interpretation methods, the effective application of this technology in hydraulic fracturing monitoring still faces certain challenges. To promote the standardized design and reasonable application of this technology, this paper reviews and summarizes the monitoring methods and technical experience of fiber-optic monitoring technology in hydraulic fracturing.
Based on field practices from 11 typical hydraulic fracturing test sites at home and abroad, this paper systematically summarizes the monitoring scheme design and data acquisition strategies of fiber-optic monitoring technology in fracturing process monitoring and post-fracturing production evaluation. First, regarding the selection of hydraulic fracturing fiber-optic monitoring schemes, this paper discusses the possible monitoring results, corresponding interpretation techniques, and technical difficulties in field implementation from three major application scenarios: in-well monitoring during fracturing, offset-well monitoring, and post-fracturing production profile monitoring. The differences among different monitoring schemes in monitoring objectives, applicable conditions, and interpretation logic are mainly analyzed, including fracture hit identification, near-wellbore fracture initiation, fracture propagation characteristics, interwell fracture communication, cluster efficiency evaluation, and post-fracturing production contribution analysis. Second, in terms of the selection of important technical parameters for hydraulic fracturing fiber-optic monitoring, based on various field practices, the selection and influence of technical parameters such as fiber deployment mode, spatial resolution, sampling interval and frequency, and monitoring well spacing in offset-well monitoring are summarized and analyzed. Finally, the methods of fiber-optic monitoring data acquisition and storage are discussed. In addition to the basic requirements of field data recording, this paper focuses on standardized data storage formats, quality control of acquired data, full lifecycle management of massive datasets, and future development directions involving integration with edge gateways, cloud platforms, and artificial intelligence-based interpretation methods.
The analysis shows that a single fiber-optic monitoring configuration is difficult to meet all monitoring objectives, and the monitoring scheme should be specifically designed according to geological and engineering conditions, the target information to be obtained, and the available interpretation methods. Therefore, clear monitoring objectives, a reasonable fiber deployment strategy, and a standardized data workflow are important prerequisites for improving the reliability and applicability of fiber-optic monitoring results. This paper provides a practical reference for the design of hydraulic fracturing fiber-optic monitoring schemes, the selection of technical parameters, and the development of interpretation methods, and also provides support for the further improvement and field application of fiber-optic monitoring technology.
Distributed Acoustic Sensing (DAS) technology, as a rapidly developing geophysical exploration method in recent years, offers advantages such as low cost, high spatial sampling density, and continuous monitoring along the entire wellbore, demonstrating significant potential in borehole Vertical Seismic Profile (VSP) applications. This paper systematically reviews the fundamental principles, acquisition methods, and seismic wave propagation characteristics of DAS technology, and analyzes its application progress and typical case studies in oil and gas exploration. By detecting phase changes in Rayleigh backscattered signals within the optical fiber, DAS enables high-sensitivity measurement of downhole strain fields and can simultaneously acquire various wavefield information, including transmitted, reflected, and scattered waves. Compared with conventional geophone-based VSP, this technology offers distinct advantages such as higher operational efficiency, flexible deployment, resilience to high temperatures and high pressures, and suitability for long-term continuous observation. However, challenges including directional sensitivity and relatively low signal-to-noise ratio still limit imaging accuracy and wavefield completeness. The paper further discusses current technical challenges, including the inherent limitations of single-component strain response and difficulties in ensuring data quality under complex wellbore conditions, and reviews recent research progress in areas such as multi-component sensing using helically wound cables, deep learning based denoising, full-waveform inversion, and integrated surface-borehole imaging. In the future, DAS-VSP technology will advance toward multi-component sensing, intelligent signal processing, full-waveform inversion, and integrated surface-borehole imaging, promoting its extensive application in full lifecycle reservoir monitoring and high-resolution structural imaging.
Distributed fiber optic acoustic sensing (DAS) has gradually become a common means for hydraulic fracturing monitoring. However, current applications and research, both domestically and internationally, primarily focus on qualitative analysis and quantitative inversion of fracturing fluid flow profile based on acoustic energy analysis. The multi-source information contained in kilohertz-range DAS data acquired from the treatment well has yet to be fully exploited. The relationships among DAS multi-frequency band response characteristics, downhole flow behavior, near-wellbore region deformation, and changes in operation parameters remain poorly understood. To more fully leverage the potential value of the in-well DAS fracturing monitoring data, this study develops a multi-frequency band feature analysis method specifically designed for in-well DAS measurements. First, low-frequency DAS extraction combined with temperature-strain decoupling algorithm is employed to obtain mechanical strain change along the treatment well, thereby revealing the distribution and evolution of near-wellbore fractures during hydraulic fracturing. Subsequently, fine spectral analysis methods for full-band DAS signal is applied to further investigate the linkage and underlying pattern among fracture evolution, operation parameters variation, and DAS response characteristics across different frequency bands. By combining the analysis of multi-frequency band feature, the presented method enables a more comprehensive interpretation of in-well fracturing monitoring DAS data than previously possible. The proposed method is applied to analyze DAS measurements acquired during the fracturing process of a horizontal well. Frequency-domain features and strain evolution information were successfully extracted, enabling the identification of downhole flow and near-wellbore region deformation events. The result indicate that: The low-frequency component of the in-well DAS signal is found to capture hydraulic fracture dynamic behavior at the individual cluster level. The 2000~3000 Hz frequency band of the DAS signal at perforating cluster locations reflects the flow behavior of fracturing fluid within hydraulic fractures. A positive correlation is observed between injection rate and both the acoustic energy and the relative band power in the 2000~3000 Hz frequency band. Compared to underperforming fluid-inflow clusters, dominant fluid-inflow clusters exhibit a distinct and sustained high-amplitude response in the DAS signals above 4000 Hz. Sand concentration is negatively correlated with both the acoustic energy and the relative band power in the 4000~5000 Hz frequency band at the perforating cluster location. This study can provide new methods for the real-time analysis of in-well DAS acoustic energy monitoring during the fracturing process. Additionally, the finding offer guidance for the rational selection of DAS sampling frequency and offer the reference for the interpretation of signal response in the field application.
Integrated CO2 Geological Storage-Geothermal Development System-Underground Energy Storage represent a novel attempt to integrate CO2 geological storage, geothermal development and compressed CO2 energy storage technologies. This approach serves as a key technology to help China fulfill its “dual carbon” commitments within a limited timeframe. To this end, this study has systematically analyzed the key theoretical foundation of the synergistic process, reviewed the technical characteristics and development status of four synergistic pathways, including CO2 geological storage coupled with plume geothermal systems, enhanced geothermal systems, aquifer thermal energy storage and compressed air energy storage, identifying the main challenges for each. Finally, a synergistic technology pathway integrating CO2 geological storage, geothermal development, and underground energy storage is proposed, and future development trends of this integrated approach are discussed. When CO2 geological storage is integrated with geothermal development and underground energy storage, it involves complex interactions among multiphase seepage, heat transfer, rock deformation, and mineralization reactions. Cyclic energy storage further complicates the spatiotemporal evolution of these physical fields. Existing synergistic technologies, including CO2 plume geothermal systems, CO2-enhanced geothermal systems, CO2 aquifer thermal energy storage systems, and compressed CO2 energy storage systems, have been extensively studied in terms of flow and heat transfer characteristics, multi-field coupling mechanisms, and system performance optimization. These studies have laid a solid foundation for the integrated technology of CO2 geological storage-geothermal development-underground energy storage. The synergy of these three components can achieve the triple objectives of low-carbon heat extraction and energy storage, representing an important direction for the low-carbon transformation of future energy systems. It is expected to become a key technology under the dual carbon goals. The main research trends are divided into four aspects: (1) Investigation of the thermal-hydraulic-mechanical-chemical coupling mechanisms in synergistic processes. (2) Analysis of porous reservoir stimulation and long-term fracture network stability. (3) Comprehensive evaluation and optimization of storage potential and heat extraction performance. (4) Intelligent lifecycle regulation of storage, heat extraction, and energy storage. With the continuous advancement of deep-earth exploration technologies, ultra-deep reservoirs are expected to become potential target sites for the integrated technology of CO2 geological storage-geothermal development-underground energy storage. Currently, the related theoretical and technological aspects of synergistic systems are still in their infancy. Urgent issues such as equipment corrosion and scaling, system adaptability to high-temperature and high-pressure environments, and synergistic mechanisms with other energy systems need to be addressed. Moving forward, it is essential to continue tackling key technological bottlenecks, such as multi-field coupling mechanisms, reservoir modification evaluation, and system performance optimization. Efforts should be made to promote the on-site application of CO2 geological storage, geothermal development utilization and underground energy storage synergistic technologies and to drive the transformation of synergistic systems toward deeper, more intelligent, and adaptive solutions for future challenges.
The lower fourth member of the Shahejie Formation (Es4x) in the southern gentle slope zone of the Dongying Sag is an important target interval for hydrocarbon exploration. However, the sedimentary infill process and the distribution patterns of favorable sand bodies remain insufficiently understood, which restricts reservoir prediction and exploration planning. Guided by the source-channel-sink system theory, this study integrates regional geological data, mud logging, wireline logging, core observations, detrital zircon geochronology, and seismic interpretation to reconstruct the paleogeomorphology and paleodrainage of the Es4x interval in the study area, systematically identify provenance areas, transport pathways, and depositional systems, and establish source-channel-sink coupling models. The results show that sediment supply during deposition of the Es4x interval was jointly derived from both proximal and distal sources. The Luxi Uplift to the southwest and the Guangrao Uplift to the southeast served as the main proximal source areas, whereas the northwestern part of the study area received distal sediment input from the Wudi Uplift. Two main types of transport pathways were recognized, namely paleovalleys and fault troughs. Fault-trough pathways dominate the western part of the study area, whereas paleovalley pathways are more common in the eastern part; the northwestern part is characterized by distal sediment transport along fault troughs. The depositional systems of the Es4x interval in the southern gentle slope zone display pronounced spatial heterogeneity. The transport and deposition of sediments from the basin margin to the lacustrine basin interior were jointly controlled by sediment supply, transport-pathway type, and depositional setting. The western and eastern parts of the study area are dominated by shallow-water delta systems controlled by proximal sediment supply, whereas the northwestern part is characterized by the development of distributary fluvial systems (DFS) under a distal-source regime. On this basis, three source-channel-sink coupling models were established: a proximal-source fault-trough transport model in the western part, a proximal-source paleovalley transport model in the eastern part, and a distal-source fault-trough transport model in the northwestern part. These models reveal how different source-channel-sink configurations control sand-body distribution patterns and the development of favorable reservoir zones. The results provide a geological basis for predicting favorable reservoirs in the Es4x interval of the southern gentle slope zone of the Dongying Sag.
The eastern margin of the Ordos Basin is rich in deep coalbed methane (CBM) resources and possesses enormous development potential. Field practice has shown that high-quality reservoirs dominated by bright coal make a significant contribution to gas production and play a key role in efficient CBM extraction. To achieve accurate and efficient identification of such reservoirs, this paper proposes an intelligent evaluation method for deep CBM horizontal well reservoirs based on elemental characteristics. Taking the No.8 coal seam in the Daniudi Gas Field of the Ordos Basin as an example, the pore structure and elemental distribution of different coal lithotypes were characterized through scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) experiments. By integrating the Pearson correlation coefficient and mutual information method, ten elemental contents including Al, S, and Ti were selected as feature parameters. Using an artificial intelligence-data-driven approach, eight machine-learning classifier models such as K-Nearest Neighbors, Random Forest, and Gradient Boosting were constructed. Research indicates that bright coal generally exhibits lower contents of elements such as Al, Si, Ti, V, and Zr, and higher contents of P, S, and Ca compared with non-bright coal. Based on confusion matrix classification evaluation metrics, the support vector machine-based deep coal rock type identification model demonstrates the optimal performance, with an F1 Score of 85.9%. Applying the optimized model to predict coal lithotypes in three horizontal wells within the block yields an accuracy exceeding 80% compared with actual drilling trajectories, which can provide a reference for fracture-stage optimization and efficient cluster placement during fracturing operations.
Under the “dual-carbon” goals, CO2 flooding, as a development method that combines enhanced oil recovery with emission reduction and efficiency improvement, is an important technological approach to promote efficient oilfield development and low-carbon transformation. Accurate identification of CO2 migration pathways and effective sweep extent are crucial for evaluating displacement effects and adjusting injection-production schemes. However, conventional production performance analysis and single monitoring methods have limited capability in characterizing the subsurface migration process and spatial distribution of CO2, making it difficult to meet the demand for fine-scale characterization. To address the problems associated with existing identification methods based on time-lapse seismic difference attributes, which are susceptible to noise, non-repeatability errors, and reservoir heterogeneity and thus often lead to scattered anomalous responses and blurred boundaries, this study proposes a method for identifying the sweep extent of CO2 flooding based on intelligent integration of time-lapse seismic multi-attributes. Difference volumes were constructed from time-lapse seismic data, and sensitive difference attributes, including amplitude, phase, and attenuation, were selected. Then, a fuzzy neural network (FNN) was introduced to perform nonlinear fusion of multiple attributes based on fuzzy rules, thereby constructing a continuous response indicator characterizing the intensity of CO2 sweep. The results show that the proposed method can effectively suppress scattered false anomalies and improve the boundary clarity and spatial connectivity of the predicted results. Time-series comparison indicates that the predicted sweep extent expanded outward from the vicinity of injection wells as injection proceeded, and migrated upward along the up-dip direction toward structurally higher positions. The delineated sweep area increased from approximately 1.7 km2 in 2010 to approximately 2.6 km2 in 2022. Validation against production performance data further shows that strong predicted responses generally correspond to the vicinity of high gas-injection wells and high gas-production wells, indicating that the proposed method can more accurately reflect reservoir response differences during the CO2 flooding process and provide an effective geophysical approach for identifying CO2 migration pathways, quantitatively characterizing sweep extent, and evaluating displacement performance.
Deep drilling operations involve complex and variable working conditions, where surface-based identification methods suffer from signal propagation delays and high misclassification rates, while limited downhole computing resources preclude the direct deployment of complex algorithms. This paper proposes a Data-driven Modified Analytic Hierarchy Process (DM-AHP) that replaces expert scoring with SHapley Additive exPlanation (SHAP) values for constructing AHP judgment matrices. The mathematical properties of SHAP values—efficiency, symmetry, linearity, and the null player property—theoretically guarantee the objectivity and multiplicative consistency of weight allocation. A “surface training-downhole identification” distributed architecture is established: model training, feature interpretation, and judgment matrix generation are performed on the surface, while only a lightweight 160-parameter matrix is deployed downhole, coupled with a boxplot-based dynamic threshold mechanism for real-time condition identification and adaptive updating. Validation on field data from 11 wells demonstrates that the model achieves 95.5% accuracy and an F1-score of 0.954 in classifying seven normal drilling conditions, with an anomaly detection F1-score of 0.931. Memory consumption is 48 KB and single inference latency is 42 ms, satisfying downhole real-time constraints. Cross-well generalization tests show that the dual dynamic update strategy—combining normalization parameter updates with adaptive threshold adjustment—improves the F1-score from 0.864 to 0.954. Compared with conventional machine learning models with parameter counts on the order of 104~106, the proposed method reduces parameters by over 99% with only a 1.7% decrease in recognition performance. The method can be directly embedded into measurement-while-drilling systems, offering a practically deployable solution that simultaneously achieves interpretability, lightweight deployment, and high accuracy for intelligent condition monitoring in deep drilling operations.
Laminations, as a key fabric feature of shale, exert a significant control on rock failure behavior and fracture propagation, thereby directly influencing the efficient development of shale oil and gas resources. However, conventional macroscopic mechanical experimental methods are strongly affected by the coupled effects of laminations, rock matrix, and natural fractures, making it difficult to isolate and identify the specific influence of lamination structures on the macroscopic mechanical properties and failure mechanisms of shale. To address this issue, this study employs a finite-discrete element method (FDEM) to establish mesoscale mechanical models that incorporate lamination distributions in shale with different lithofacies. These models are used to quantitatively characterize the effects of lamination orientation, thickness, and number on the uniaxial compressive strength, elastic modulus, fracture modes, and fracture morphology of shale. The results indicate that: (1) Hard laminations are the key factor governing shale mechanical properties. After introducing hard laminations into the shale models, the uniaxial compressive strength increases by 17.6%~35.2%, and the elastic modulus increases by 28.0%~39.8%, accompanied by an increase in fracture complexity. (2) As the thickness of horizontal and vertical hard laminations increases from 600 μm to 2000 μm, the elastic modulus and uniaxial compressive strength of shale increase by 19% and 4.8%, respectively, while the proportion of Mode I tensile fractures decreases by up to 70%. When the lamination thickness is ≤ 800 μm, macroscopic shear fractures are formed together with complex tensile branching fractures, whereas when the lamination thickness is ≥ 1500 μm, the number of branching fractures gradually decreases. (3) As the number of hard laminations increases from 5 to 25, the elastic modulus and compressive strength increase by 37.8% and 8.0%, respectively, while the proportion of Mode I tensile fractures decreases by 47.1%, and the number of branching fractures increases, resulting in enhanced fracture complexity. (4) Higher mechanical properties of hard laminations lead to a more pronounced strengthening effect on the shale. The greater the mechanical contrast between the rock matrix and hard laminations, the more significant the impact on macroscopic strength. In addition, with increasing lamination strength, the dominant failure mode gradually transitions from mode I tensile fracture to a mixed mode I-II fracture mode, accompanied by fewer branching fractures. The findings are expected to provide a theoretical basis for an in-depth understanding of the influence mechanism of shale lamination fabric on its macroscopic mechanical properties.
The rheological behavior of drilling fluids in ultra-deep wells under wide temperature-pressure conditions is difficult to characterize with a single rheological model. Existing methods typically select different rheological models for different temperature intervals and regress rheological parameters separately, resulting in discontinuous jumps in rheological parameters at segmentation temperatures and compromising the accuracy of wellbore pressure calculations. To address this issue, four drilling fluid systems of oil-based and water-based with two density levels were prepared, and rheological experiments were conducted under 195 temperature-pressure conditions spanning 20~200 °C and 0.1~200 MPa, yielding a total of 1170 shear stress data points. Four fundamental patterns governing the variation of shear stress with temperature, pressure, and shear rate were identified, from which seven physical constraint conditions were distilled, covering shear-thinning characteristics, temperature-pressure monotonicity, and coupling effects between temperature-pressure and shear rate, and the relative magnitude of temperature versus pressure effects. To overcome three shortcomings of standard genetic programming-symbolic regression-a vast search space, a lack of physical constraints, and insensitivity of a single error metric to the low-shear-rate region-three improvements are proposed: Injecting classical constitutive models as skeletal seeds into the initial population, embedding seven physical constraints as dynamically weighted penalty terms in the fitness function, and constructing a multi-component loss function. A shear stress prediction formula with temperature, pressure, and shear rate as independent variables was thereby established. The formula comprises three components-a yield stress term, a viscous flow term, and a temperature-pressure coupling term-with coefficients of determination of 0.9933 and 0.9948 for the training dataset and independent validation set, respectively, mean relative errors of 6.51% and 6.21%, and an overall satisfaction rate of the seven physical constraints of 99.28%. A transfer strategy of fixing the formula structure and refitting only the coefficients was validated on five additional drilling fluid systems of different types and densities; the mean relative error for each system ranged from 7.81%~9.70%, with a cross-system coefficient of variation of 0.069. The formula was applied to the wellbore pressure calculation workflow and validated on two ultra-deep wells; the mean absolute error was reduced from 0.56~2.06 MPa with the piecewise rheological model method to 0.19~1.27 MPa, a reduction of 38%~66%, while the abrupt change in unit frictional pressure drop at segmentation temperature was reduced from approximately 38% to less than 0.1%.
In reservoir stress field simulation, Physics-Informed Neural Networks (PINN) can achieve high-precision unsupervised solutions; however, their model structure tightly couples the computational domain with physical parameters, resulting in applicability only to a fixed set of material properties. This leads to limited generalization under varying working conditions. To enhance the generalization capability of PINN, this study develops an intelligent computational approach based on the Physics-Informed Deep Operator Network (PI-DeepONet). By introducing a branch-trunk architecture and employing the Hadamard product to fuse parameter and coordinate features, an end-to-end mapping from reservoir physical parameters to the stress-displacement field is established. Furthermore, a hard-constraint mechanism and a staged progressive training strategy are integrated to construct a stress field operator model with strong generalization capacity. The results demonstrate that this method overcomes the non-generalizability of conventional PINN, achieving approximately 30.6% improvement in training efficiency under sparse physical-space discretization, and around 62.4% enhancement in prediction accuracy under dense discretization by applying hard constraints. This research provides a reliable intelligent computational framework for efficient hydrocarbon reservoir development and CO2 geological storage assessment.
In the fracturing stimulation of highly deviated wells within the sand-mud thin interbeds of the L-3 formation in the Weizhou Oilfield of the South China Sea, the selection of perforation targets directly determines the cross-layer fracture propagation effect and the effective stimulation degree. Such reservoirs are characterized by well-developed laminated weak planes, rapid vertical lithological variation, and complex interlayer stress constraints, which make it difficult to quantitatively identify optimal fracturing targets. To address this problem, triaxial compression tests, pre- and post-experiment CT scanning, and XRD tests were conducted to investigate the failure modes and fracture propagation characteristics of four lithologies: fine sandstone, siltstone, argillaceous siltstone, and mud shale. Furthermore, integrating logging-interpreted rock mechanical parameters, in-situ stress parameters, mineral composition, and well trajectory data, this study constructed a feature system for fracture height prediction oriented to the optimization of fracturing targets. Within the AutoML framework, the GBR, HGBR and MLP models were screened and optimized. The results reveal that laminations and natural weak structures remarkably enhance rock heterogeneity, reduce rock strength, and dominate fracture initiation locations and propagation paths. Among them, high-density laminations developed in argillaceous siltstone and mud shale tend to induce fractures to propagate or deflect along weak planes, acting as the key factor causing differences in fracturing response at various perforation targets. Model evaluation indicates that the MLP model achieves the minimum RMSE of 6.37 in the cross-validation stage, while the GBR model delivers the best performance on the independent test set, with RMSE and MAE of 3.71 and 3.59 respectively. It is verified that the established models can provide quantitative support for the fracture height evaluation of perforation targets under small sample conditions. The application results of Well W1 show that fracture height is negatively correlated with interlayer stress difference. When the interlayer stress difference is less than 2 MPa and the vertical-horizontal stress difference exceeds 20 MPa, cross-layer fractures with a height of more than 40 m are more likely to be formed. In continuous argillaceous siltstone reservoirs, high-brittleness intervals with B1>0.6 and B2>80% are more favorable for acquiring a larger fracture height. This study concludes that low interlayer stress difference, high vertical-horizontal stress difference, and high brittleness are important discrimination indicators for high-quality fracturing targets of highly deviated wells in the L-3 sand-mud thin interbeds of the Weizhou Oilfield. The proposed fracture height prediction and target evaluation method can provide a reliable basis for the optimization of perforation location, the ranking of fracturing intervals, and the optimization of construction parameters in offshore thin interbedded reservoirs.
The ZJ10A and ZJ10B formations of the L Oilfield in the Pearl River Mouth Basin are the main low-permeability reef limestone reservoirs. The average measured core permeability of the two limestone reservoirs, A and B, in the ZJ10 layer is 45.9 mD, with a median value of 21.5 mD. The reservoirs are characterized by strong heterogeneity and well-developed fractures. In the early stage of oilfield development, problems such as rapid water breakthrough, significant production decline, and energy depletion were encountered. As the first water-flooded fractured low-permeability reef limestone oilfield in the eastern South China Sea, it has low well-pattern density, few coring intervals, and insufficient imaging logging data. With traditional methods, it is difficult to accurately characterize small-scale inter-well fractures. The main controlling factors and development patterns of fractures are unclear, which restricts the efficient development of the reservoir. To address the above difficulties, guided by reservoir geomechanics theory, this paper integrates multi-source data and constructs an integrated technical system of “paleo-petrophysical parameter correction - in-situ stress simulation-fracture prediction-development countermeasures”. Based on the background of the Dongsha movement, the paleo-burial depth (600~700 m shallower than the present) was restored, and the paleo-petrophysical parameters were obtained by correcting the present logging data. Finite element numerical simulation was adopted, with a 33 MPa northwestward paleo-stress applied to quantify the controlling effect of stress on fractures. The reliability of the prediction results was verified using the fracture development is only enhanced at image logging and actual drilling data. The results show that the target reservoirs are mainly developed with two sets of high-angle conjugate shear structural fractures (accounting for 91%) trending northwest and nearly north-south, whose formation is controlled by the northwestward compressive stress during the Dongsha Movement. Laterally, the high fracture development areas are concentrated in the structural highs in the east of Well 1d and the south of Well 2d, and the fracture development is only enhanced at the turning points of the southern fault. Vertically, the fracture density of the ZJ10A formation (0.13~0.31 fractures/m) is higher than that of the ZJ10B formation (0.04~0.28 fractures/m), and it decreases from top to bottom. The fracture prediction results were verified by actual drilling data, with the absolute error of 82% of the samples being ≤ 0.7 fractures/m, indicating reliable prediction. Meanwhile, there was a positive correlation between the fracture density and the water cut rise rate in the early production stage. Based on this, the production wells were divided into 4 types, and corresponding oil stabilization and water control measures were proposed for each type. The integrated technical process constructed in this study effectively solved the problem of quantitative prediction of small-scale fractures in low-permeability reef reservoirs under the condition of few actual measurement data. The study indicates that the constructed integrated technical system effectively solves the problem of quantitative prediction of small-scale fractures in low-permeability reef limestone under the condition of few offshore wells and improves the relevant technical system. The clarified fracture distribution patterns and differentiated oil stabilization and water control countermeasures for the 4 types of wells can provide a scientific basis for the development adjustment of the L Oilfield and also provide technical reference for the fine exploration and efficient development of similar reservoirs at the Sea.
Taking a deep sandstone reservoir in China as the study object, the pore structure of 19 core samples was quantitatively characterized at multiple scales using cast thin sections, scanning electron microscopy (SEM), high-pressure mercury intrusion (HPMI), and nuclear magnetic resonance (NMR). Based on pore-throat parameters derived from HPMI and NMR, the reservoir was classified into four types (I-IV), with reservoir quality and seepage capacity progressively decreasing and pore-throat complexity and heterogeneity correspondingly increasing. Fractal theory was further introduced to calculate fractal dimensions from both HPMI and NMR, thereby elucidating the relationships among petrophysical properties, pore-throat characteristics, and fractal parameters. Results indicate that the reservoir is dominated by primary intergranular pores and is overall characterized by moderate porosity and low permeability with pronounced heterogeneity. The HPMI fractal curve exhibits no segmentation, with fractal dimensions ranging from 2.6602 to 2.8428 (average 2.7508), all exceeding 2.5, suggesting a continuous pore-throat size distribution but strong heterogeneity and poor connectivity. The HPMI fractal dimension is negatively correlated with porosity and permeability, while positively correlated with displacement pressure and mercury withdrawal efficiency. In contrast, the NMR fractal curve shows a two-segment pattern: The fractal dimension of effective pores ranges from 0.8034 to 1.9995 (average 1.4163) and shows no clear correlation with petrophysical properties, whereas the fractal dimension of movable pores ranges from 2.7232 to 2.9773 (average 2.8644) and is negatively correlated with porosity and permeability. The HPMI fractal dimension is in good agreement with the NMR movable-pore fractal dimension in terms of both numerical range and variation trend, and both effectively characterize the complexity of the connected pore system. These results provide a basis for the classification, evaluation, and fine-scale development of deep sandstone reservoirs.
Low-permeability reservoirs exhibit highly complex and strongly variable pore systems. The pronounced nonlinear coupling between pore-structure attributes and permeability further complicates displacement process screening. To establish a pore-structure classification framework and identify suitable displacement schemes, this study applies data-mining techniques to mercury intrusion capillary pressure data from 470 low-permeability tight sandstone cores collected from a target block of the Yanchang Formation in the Ordos Basin. The pore systems are classified into four types: large pore-size with weak heterogeneity, large pore-size with strong heterogeneity, small pore-size with weak heterogeneity, and small pore-size with strong heterogeneity. Representative pore-size distributions for each type are then used to build capillary-bundle models for displacement simulations. A threshold pressure differential is adopted as the criterion to delineate the favorable operating windows for gas flooding and water flooding, and the performance of the two processes is quantitatively evaluated. The results show that the relative superiority of gas flooding versus water flooding is mainly controlled by the coupled effects of pore size, heterogeneity, and injection pressure differential. For large pore-size systems with weak heterogeneity, gas flooding is preferred at low pressure differentials; as the pressure differential increases, water flooding gradually becomes dominant due to improved front stability and a larger swept pore volume, and the threshold pressure differential increases with increasing pore volumes injected. In contrast, strong heterogeneity in large pore-size systems significantly intensifies gas channeling and early breakthrough, narrowing the pressure range in which gas flooding is advantageous. For small pore-size systems, effective water flooding requires sufficiently high pressure differentials and becomes advantageous only under relatively large pressure gradients, making operational feasibility a key constraint. In small pore-size systems with strong heterogeneity, heterogeneity further aggravates gas channeling while reducing the incremental benefit of increasing waterflood pressure; the threshold pressure differential remains high. When allowable pressure differentials are limited, low-pressure gas flooding may be prioritized as a practical alternative. Overall, the proposed workflow integrating pore-structure classification and displacement-performance evaluation provides a quantitative basis for displacement process screening and enhanced oil recovery strategy design in low-permeability reservoirs under injectivity constraints.
This study focuses on the Longwangmiao Formation of the Moxi Block in the Anyue Gas Field, Sichuan Basin—a large carbonate gas reservoir with active edge water. Addressing the challenges of large-scale, low-amplitude, and strongly heterogeneous edge-water gas reservoirs—characterized by wide gas-water transition zones, complex water invasion patterns, and the high computational cost of conventional numerical simulation that limits precise water control—this work systematically investigates reservoir dynamic diagnosis, history matching, and production optimization based on streamline. A gas-phase streamline tracing method was developed to quantitatively and intuitively characterize subsurface gas-water migration paths and dynamic well control areas. Furthermore, a hierarchical multiscale automatic history-matching workflow was proposed, which sequentially calibrates the layer-level, regional, and grid parameters. This approach significantly improves the history-matching accuracy of bottom-hole pressure and cumulative water production, effectively capturing reservoir heterogeneity and water-invasion channels. On this basis, a streamline-based rate allocation optimization algorithm was further developed. By dynamically calculating the movable water-gas ratio for each well and reservoir field, the algorithm enables real-time adjustment of production rates among wells under a constant total gas production rate constraint. This strategy effectively controls water production and substantially enhances gas recovery. Field optimization results show that after 4.5 years of production control, gas recovery increased by 9.06%, cumulative water production decreased by 64.06%, and the remaining gas distribution was significantly improved. Compared with conventional numerical simulation, the proposed intelligent streamline technique enables intuitive and quantitative characterization of subsurface fluid flow, achieves auto-history matching and intelligent production forecasting, greatly reduces manual workload, minimizes subjective errors from human intervention, and provides a novel technical approach for precise gas reservoir management and control.
Stable and reliable calculation of phase envelopes for hydrocarbon systems is a key and challenging aspect of reservoir fluid phase behavior research. The saturation pressure curve tracing algorithm proposed by Michelsen represents a milestone contribution in this field and is still widely used today. However, this method involves complex technical details, making it difficult to implement correctly, and is prone to non-convergence issues during computation, which may prevent the generation of a complete phase diagram. This paper presents a novel method for accurately constructing phase diagrams of oil and gas hydrocarbon systems based on phase stability analysis combined with the bisection method. In this approach, phase stability analysis is first used to compute the cricondenbar and the cricondentherm on the phase envelope. Based on these two points, the pressure-temperature plane is divided into four regions. Within each region, the phase boundary is located using a combination of phase stability analysis and the bisection method, ultimately leading to the construction of a complete phase diagram. The proposed method is applied to compute phase diagrams of various reservoir fluids, and the results are compared with those obtained using Michelsen’s method. The comparison demonstrates that the proposed algorithm is capable of calculating phase diagrams of oil and gas hydrocarbon systems reliably and stably.
Against the backdrop of China’s “dual carbon” goals and the clean energy transition, oil and gas production — as a sector characterized by high energy consumption and high carbon emissions — faces mounting pressure to reduce its carbon footprint. How to substantively advance the low-carbon transformation of oil and gas production without compromising energy security has emerged as a critical challenge for the industry. Currently, oil and gas operations remain heavily dependent on fossil fuels for self-consumption. The transition toward clean energy is inherently constrained by the intermittency of renewable energy output and the continuous, stable energy demands of production processes. Compounded by the complexity of oilfield scenarios and low technological compatibility, clean energy substitution confronts a dual dilemma of “difficult grid absorption” and “difficult technology matching”. To address these challenges, this paper systematically analyzes energy consumption patterns and evaluates clean substitution potential across three typical production scenarios: light oil, heavy oil, and natural gas. To reconcile the conflict between fluctuating renewable energy output and the steady-state operational requirements of production processes, and to overcome the compatibility barriers between scenario-specific energy characteristics and clean energy supply technologies, this study constructs a mapping framework of “oil and gas scenario - energy consumption structure - substitution technology”. Through this framework, the differentiated energy consumption structures across various scenarios are examined in depth, and three core technological pathways for clean energy substitution are proposed: “diversified supply and intelligent consumption of green electricity”, “clean substitution of high-grade and medium-to-low temperature thermal energy”, and “multi-energy complementarity and system integration”. Advancing scenario-specific clean energy substitution tailored to local conditions constitutes a key engine for transforming oil and gas production into integrated energy bases and achieving green, low-carbon development across the industry.
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