摘要：

超深井普遍存在高温高压、储层非均质性强等特征，井筒内气水两相流体物性随地层温压动态变化，使得产出剖面测井响应表现出强非线性规律。现有的滑脱模型和漂流模型等常规气水两相产出剖面解释模型均依托地面常温常压实验而建立，无法精准刻画井下高温高压环境下流体产出情况，存在解释误差大、适用性差等问题，难以支撑超深层气藏精细动态评价。现有的智能解释方法多采用涡轮流量单一约束，缺少温度、压力与密度多测井信息协同调控，且没有区分不同测井仪器分辨率与测量可信度差异，容易造成底水识别、产能解释失真。因此，本文提出一种多源测井约束与海鸥优化融合的高温高压超深井气水两相产出剖面解释方法，通过将各深度实测井下温压动态计算得到的高温高压气水物性参数引入到温度测井、压力测井和密度测井的约束模型中，克服传统解释模型采用地面常温常压固定物性参数带来的系统偏差。该方法以各射孔层气水各相流量为待求解变量，利用井筒能量守恒、多相流动量平衡机理分别建立温度、压力和密度测井的理论响应模型，并综合测井仪器分辨率与可信度系数建立高维非线性的目标函数，同时，将涡轮流量测井作为等式约束条件，采用海鸥优化算法(SOA)完成迭代计算，实现各射孔层气水流量同步求解。实际超深井产出剖面测井资料验证结果表明，该方法计算地面产气量、产水量与地面计量数据的相对误差仅为5.23%、6.06%，满足产出剖面资料处理要求，可为超深层油气藏产能定量评价、堵水调剖与动态监测提供可靠的技术支撑。

关键词: 生产测井, 超深井, 气水两相流, 多源测井约束, 海鸥优化算法

Abstract:

Ultra-deep wells are generally characterized by high temperature, high pressure, and strong reservoir heterogeneity. The physical properties of gas-water two-phase fluids within wellbores vary dynamically with formation temperature and pressure, leading to highly nonlinear responses in production profile logging measurements. Conventional gas-water two-phase interpretation models, such as the slip model and drift-flux model, are developed based on laboratory experiments under surface ambient temperature and pressure. These models fail to accurately describe fluid production behavior under downhole high-temperature and high pressure conditions, accompanied by large interpretation errors and limited applicability, which restricts the fine dynamic evaluation of ultra deep gas reservoirs flow logging as a single constraint, lacking collaborative regulation of multi-logging data including temperature, pressure and density. Moreover, these methods ignore the differences in resolution and measurement reliability among various logging instruments, which readily results in distorted identification of bottom water and inaccurate productivity interpretation. To address the above limitations, this study proposes an interpretation method for gas-water two-phase production profiles of high temperature and high pressure ultra deep well that integrates multi-source logging constraints with the Seagull Optimization Algorithm(SOA), the model embeds high temperature and high pressure gas-water physical properties dynamically calculated from measured downhole temperature-pressure at each depth into temperature logging, pressure logging and density logging response models, and introduces the gas-phase Joule-Thomson thermal effect to characterize the thermal behavior of high pressure gas in wellbore, which overcomes the systematic deviation caused by fixed physical parameters under surface ambient temperature and pressure adopted in traditional models. Taking the gas and water volumetric flow of each perforated interval as unknown variables to be solved, theoretical response models for temperature, pressure and logging and density are separately constructed based on wellbore energy conservation and multiphase momentum balance principles. A high-dimensional nonlinear objective function is further established by integrating the resolution and reliability coefficients as an equality constraint. The seagull optimization algorithm(SOA) is adopted to implement iterative computation, enabling simultaneous solution of gas and water flow for all perforated intervals. Validation using field production profile logging data form actual ultra deep wells demonstrates that the relative errors between the total gas and water production calculated by the proposed method and surface metering data are only 5.23% and 6.06%, respectively, which fully meets the processing specifications of production logging data. The proposed method can provide reliable technical support for quantitative productivity evaluation, water shutoff and profile control, as well as long term dynamic monitoring of ultra deep oil and gas reservoirs.

Key words: production logging, ultra-deep well, gas-water two-phase flow, multi-source logging constraints, seagull optimization algorithm

中图分类号: 

• P618.13

• TE151