Akram Saima, Akhter Gulraiz, Ge Yonggang, Azeem Tahir
Department of Earth Sciences, Quaid-I-Azam University, Islamabad 45320, Pakistan.
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China.
ACS Omega. 2024 Jan 12;9(4):4775-4791. doi: 10.1021/acsomega.3c08169. eCollection 2024 Jan 30.
The endeavor to explore and characterize oil and gas reservoirs presents significant challenges due to the inherent heterogeneities that are further compounded by the existence of thin sand layers encapsulated in shale strata. This complexity is intensified by limited and low-resolution seismic data, missing critical well-log information, and inaccessible angle stack data. Conventional reservoir classification approaches have struggled to address these issues, primarily due to their limitations in handling missing data effectively and, hence, precise estimations. This study focuses on the characterization of thin, heterogeneous potential sands of the B-interval within the Lower Goru Formation, a proven gas reservoir in the Badin area. The reservoir sands with varying thicknesses are assessed in detail for their optimized description and field productions by handling challenges, including low seismic resolutions, heterogeneities, and missing data sets. An innovative solution is developed based on the integration of continuous wavelet transform (CWT) and machine learning (ML) techniques for the approximation of missing data sets, i.e., S-wave (DTS), along with enhanced elastic and petrophysical properties. The improved properties are augmented by the high resolution attained by CWT and captured variability more profoundly through the implication of residual neural networks (ResNet). The limitations of conventional approaches are harnessed by ML solutions that operate with limited input data and deliver significantly improved results in characterizing enigmatic thin sand reservoirs. The high-frequency petroelastic properties reliably determined the thin heterogeneous potential sand bodies and illuminated a channelized play fairway that can be tested for additional wells with low-risk involvement.
由于存在固有非均质性，且页岩地层中包裹着薄砂层，这进一步加剧了非均质性，因此勘探和表征油气藏面临重大挑战。有限且低分辨率的地震数据、关键测井信息缺失以及无法获取的角度叠加数据，加剧了这种复杂性。传统的油藏分类方法难以解决这些问题，主要是因为它们在有效处理缺失数据以及进行精确估算方面存在局限性。本研究聚焦于下戈鲁组B段薄的、非均质潜在砂岩的表征，该地层是巴丁地区一个已证实的气藏。通过应对包括低地震分辨率、非均质性和数据集缺失等挑战，对不同厚度的储层砂岩进行详细评估，以实现其优化描述和油田生产。基于连续小波变换（CWT）和机器学习（ML）技术的集成，开发了一种创新解决方案，用于近似缺失数据集，即S波（DTS），同时增强弹性和岩石物理性质。通过CWT获得的高分辨率增强了这些改进的性质，并通过残差神经网络（ResNet）更深刻地捕捉到变异性。传统方法的局限性被ML解决方案克服，该方案在有限输入数据下运行，并在表征神秘的薄砂层油藏方面取得了显著改进的结果。高频岩石弹性性质可靠地确定了薄的非均质潜在砂体，并揭示了一条可用于低风险参与的额外井测试的通道化有利储集层。
