Petrophysical Modelling of Structure-Cum-Stratigraphic Play for Improved Reservoir Potential, an Integrated Field Study of L. Goru Sands, Pakistan

M. F. A. H. Khan, M. Abid, A. Fareed, Z. Javed, M. N. Khan, Shariq Hashmi
{"title":"Petrophysical Modelling of Structure-Cum-Stratigraphic Play for Improved Reservoir Potential, an Integrated Field Study of L. Goru Sands, Pakistan","authors":"M. F. A. H. Khan, M. Abid, A. Fareed, Z. Javed, M. N. Khan, Shariq Hashmi","doi":"10.2118/196066-ms","DOIUrl":null,"url":null,"abstract":"\n Technical evaluation and subsequently devising an appraisal and development strategy of a structural cum stratigraphic reservoir based on a discovery well only is always challenging. The reservoir under discussion was discovered as a structurally bounded trap and the appraisal wells were drilled on NW-SE direction along with the main bounding fault based on this understanding. However, presence of hydrocarbon below the spill point, anomalous sand thickness, lateral facies and reservoir quality variations observed in few of the wells indicated stratigraphic component in the field. Further complexity was added when the deepest tested gas was assigned on the structural map which showed extension of the hydrocarbon play outside the block boundary where the area was under different operating company that later drilled multiple wells near the block boundary. Therefore, it was critical to estimate correct initial gas in-place and percentage distribution of hydrocarbon across the lease boundaries.\n Figure 1 Well location map for the studied field\n \n \n The objective of this paper is to present workflow that integrates multiple dataset to understand the field's hydrocarbon filling mechanism. Detailed geophysical and Petrophysical work has been carried out, which includes building of sequence stratigraphic framework, preparation of seismic attribute maps, understanding of the depositional setting for all the individual sand units encountered in all the wells, rock quality assessment (core and log methods with integration of capillary pressure curves), free water level (FWL) assessment, permeability modelling using machine learning approach (NN), pore throat radius estimation to relate hydrocarbon filling mechanism and saturation-height function modelling to build consistent 1D water saturation model.\n \n \n \n Comprehensive dataset has been acquired to evaluate the potential of the field that covers 3D seismic for the entire field, biostratigraphic analysis for seven (7) well, conventional logs in twelve (12) wells and advance measurements like Elemental Capture Spectroscopy and high-resolution resistivity images in five (5) wells. Core analysis data also acquired in five (5) different wells including routine core analysis, capillary pressure measurements using high pressure mercury injections, pore throat radius, relative permeability measurements (Centrifuge), formation resistivity factor measurements and sedimentological analysis (XRD & thin section) to overcome the challenges and defining the uncertainty associated with initial gas in-place.\n \n \n \n Sequence based boundaries were defined to correlate individual sand bodies using the core data, image logs, elastic logs, seismic transacts and attribute maps for understanding the depositional setting. Lat-er these correlations were used to build a consistent petrophysical model including VCL estimation from Gamma/Neutron-Density/Sonic Density methods which was validated with ECS/XRD data. Porosity model was developed and validated from the core porosity followed by variable \"m\" estimation from the porosity/m relationship using the SCAL data. Later on, the consistent water saturation (Sw) models were built for all the studied wells. Permeability models were built using Neural Network (NN) where core-based permeability used for calibration and the model was tested qualitatively with the mobility and the well test permeability. For the validation of Sw from the logs, capillary pressure-based flow units were built using FZI/RQI, Winland & BVW (log) methods to define flow units defined through the core data. It was observed that the Winland R35 method-based pore throat radius had good correlation with the Sw log. FWL from MDT to estimate the height of the gas column, Skelt Harrison equation to capture the shape of the capillary pressure curve and Swi from the Centrifuge analysis were used to calibrate MICP end point which helped in building consistent Saturation-height functions. Results showed good to excellent match from the modeled Sw (Pc) vs Sw(log).\n","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, October 01, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/196066-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Technical evaluation and subsequently devising an appraisal and development strategy of a structural cum stratigraphic reservoir based on a discovery well only is always challenging. The reservoir under discussion was discovered as a structurally bounded trap and the appraisal wells were drilled on NW-SE direction along with the main bounding fault based on this understanding. However, presence of hydrocarbon below the spill point, anomalous sand thickness, lateral facies and reservoir quality variations observed in few of the wells indicated stratigraphic component in the field. Further complexity was added when the deepest tested gas was assigned on the structural map which showed extension of the hydrocarbon play outside the block boundary where the area was under different operating company that later drilled multiple wells near the block boundary. Therefore, it was critical to estimate correct initial gas in-place and percentage distribution of hydrocarbon across the lease boundaries. Figure 1 Well location map for the studied field The objective of this paper is to present workflow that integrates multiple dataset to understand the field's hydrocarbon filling mechanism. Detailed geophysical and Petrophysical work has been carried out, which includes building of sequence stratigraphic framework, preparation of seismic attribute maps, understanding of the depositional setting for all the individual sand units encountered in all the wells, rock quality assessment (core and log methods with integration of capillary pressure curves), free water level (FWL) assessment, permeability modelling using machine learning approach (NN), pore throat radius estimation to relate hydrocarbon filling mechanism and saturation-height function modelling to build consistent 1D water saturation model. Comprehensive dataset has been acquired to evaluate the potential of the field that covers 3D seismic for the entire field, biostratigraphic analysis for seven (7) well, conventional logs in twelve (12) wells and advance measurements like Elemental Capture Spectroscopy and high-resolution resistivity images in five (5) wells. Core analysis data also acquired in five (5) different wells including routine core analysis, capillary pressure measurements using high pressure mercury injections, pore throat radius, relative permeability measurements (Centrifuge), formation resistivity factor measurements and sedimentological analysis (XRD & thin section) to overcome the challenges and defining the uncertainty associated with initial gas in-place. Sequence based boundaries were defined to correlate individual sand bodies using the core data, image logs, elastic logs, seismic transacts and attribute maps for understanding the depositional setting. Lat-er these correlations were used to build a consistent petrophysical model including VCL estimation from Gamma/Neutron-Density/Sonic Density methods which was validated with ECS/XRD data. Porosity model was developed and validated from the core porosity followed by variable "m" estimation from the porosity/m relationship using the SCAL data. Later on, the consistent water saturation (Sw) models were built for all the studied wells. Permeability models were built using Neural Network (NN) where core-based permeability used for calibration and the model was tested qualitatively with the mobility and the well test permeability. For the validation of Sw from the logs, capillary pressure-based flow units were built using FZI/RQI, Winland & BVW (log) methods to define flow units defined through the core data. It was observed that the Winland R35 method-based pore throat radius had good correlation with the Sw log. FWL from MDT to estimate the height of the gas column, Skelt Harrison equation to capture the shape of the capillary pressure curve and Swi from the Centrifuge analysis were used to calibrate MICP end point which helped in building consistent Saturation-height functions. Results showed good to excellent match from the modeled Sw (Pc) vs Sw(log).
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
巴基斯坦L. Goru砂岩储层物理模拟研究
仅根据一口发现井对构造地层油藏进行技术评价,并制定评价和开发策略,一直是一项具有挑战性的工作。根据这一认识,发现储层为构造封闭型圈闭,并沿主封闭性断层向北西-东向钻探评价井。然而,在少数油井中观察到的泄漏点以下的油气存在、异常的砂层厚度、侧向相和储层质量变化表明了该油田的地层组成。当在构造图上指定了最深的测试天然气时,复杂性进一步增加了,该构造图显示了油气区延伸到区块边界之外,该区域由不同的运营公司负责,后来在区块边界附近钻了多口井。因此,正确估计初始含气量和油气在租界内的分布百分比至关重要。本文的目的是提出集成多个数据集的工作流程,以了解该油田的油气填充机制。详细的地球物理和岩石物理工作已经开展,包括建立层序地层格架、编制地震属性图、了解所有井中所有砂单元的沉积背景、岩石质量评估(结合毛细管压力曲线的岩心和测井方法)、自由水位(FWL)评估、利用机器学习方法(NN)建立渗透率模型、孔喉半径估算,将油气充注机理与饱和高度函数建模联系起来,建立一致的一维含水饱和度模型。为了评估该油田的潜力,研究人员获得了全面的数据集,包括整个油田的3D地震数据、7口井的生物地层分析数据、12口井的常规测井数据以及5口井的元素捕获光谱和高分辨率电阻率图像等先进测量数据。此外,还获得了5口不同井的岩心分析数据,包括常规岩心分析、高压压汞毛细管压力测量、孔喉半径、相对渗透率测量(离心机)、地层电阻率系数测量和沉积学分析(XRD和薄片),以克服挑战,确定与初始气体相关的不确定性。利用岩心数据、图像测井、弹性测井、地震数据和属性图,定义了基于层序的边界,将单个砂体联系起来,以了解沉积背景。随后,这些相关性被用于建立一致的岩石物理模型,包括伽马/中子密度/声波密度方法的VCL估计,并用ECS/XRD数据进行验证。根据岩心孔隙度建立孔隙度模型并进行验证,然后利用SCAL数据根据孔隙度/m关系估算变量“m”。随后,对所有研究井建立了一致含水饱和度(Sw)模型。利用神经网络(NN)建立渗透率模型,其中基于岩心的渗透率进行校准,并利用流动性和试井渗透率对模型进行定性测试。为了从测井资料中验证Sw,利用FZI/RQI、Winland和BVW(测井)方法建立了基于毛细管压力的流动单元,以定义通过岩心数据定义的流动单元。结果表明,基于Winland R35方法的孔喉半径与测井曲线具有较好的相关性。MDT的FWL用于估计气柱高度,Skelt Harrison方程用于捕获毛管压力曲线的形状,离心分析的Swi用于校准MICP端点,这有助于建立一致的饱和度-高度函数。结果表明,模拟的Sw(Pc)与Sw(log)匹配良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Verification of Autonomous Inflow Control Valve Flow Performance Within Heavy Oil-SAGD Thermal Flow Loop Reactive vs Proactive Intelligent Well Injection Evaluation for EOR in a Stratified GOM Deepwater Wilcox Reservoir using Integrated Simulation-Surface Network Modeling A Novel Workflow for Oil Production Forecasting using Ensemble-Based Decline Curve Analysis An Artificial Intelligence Approach to Predict the Water Saturation in Carbonate Reservoir Rocks Characterization of Organic Pores within High-Maturation Shale Gas Reservoirs
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1