Hongquan Chen , Changdong Yang , Akhil Datta-Gupta , Jianye Zhang , Liqun Chen , Lei Liu , Baoxin Chen , Xiaofei Cui , Fashun Shi , Asnul Bahar
{"title":"Fracture inference and optimal well placement using a multiscale history matching in a HPHT tight gas reservoir, Tarim Basin, China","authors":"Hongquan Chen , Changdong Yang , Akhil Datta-Gupta , Jianye Zhang , Liqun Chen , Lei Liu , Baoxin Chen , Xiaofei Cui , Fashun Shi , Asnul Bahar","doi":"10.1016/j.upstre.2020.100002","DOIUrl":"10.1016/j.upstre.2020.100002","url":null,"abstract":"<div><p><span>Fractures play an important role in well placement by influencing the well productivity and dominating the fluid flow underground. Though seismic data<span> is often used to identify fracture swarms, the conductivities of fractures can be hard to evaluate, and data quality of seismic surveys typically decreases as the reservoir becomes deeper. In terms of inferring complex fracture patterns, dynamic production data integration can play a vital role. This paper presents a hierarchical multi-scale history matching approach that combines evolutionary algorithm and streamline method to calibrate </span></span>fracture permeabilities<span> in a HPHT tight gas reservoir using dual porosity<span><span> models. The reservoir is located in the Tarim basin, China, and has a depth of more than 7500 m with high pressure (18000 psi) and high temperature (340 °F). The fracture properties of the dual porosity model are initially derived from </span>seismic attributes and further calibrated with dynamic data using the proposed multi-scale history matching. The calibrated fracture model can detect the fracture swarm locations underground. The streamlines generated from the history matched model in conjunction with reservoir properties are used to define a ‘depletion capacity map’ which is then used for optimal infill well placement.</span></span></p><p>Most of the previous streamline-based field applications are limited to incompressible or slightly compressible flow. In this paper streamline-based analytical sensitivities are extended to highly compressible flow. To our knowledge, this is the first-time streamlines have been used to facilitate history matching and optimal well placement for gas reservoirs.</p></div>","PeriodicalId":101264,"journal":{"name":"Upstream Oil and Gas Technology","volume":"2 ","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.upstre.2020.100002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"105403859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling of gas flow in fractured shale","authors":"Richard Wheaton","doi":"10.1016/j.upstre.2020.100001","DOIUrl":"https://doi.org/10.1016/j.upstre.2020.100001","url":null,"abstract":"<div><p><span>In this paper equations and methodologies for the simple modeling of gas flow<span> in fractured shale are developed. Transmissibility<span> following hydraulic fracturing is related to the geo-mechanical properties of the shale. Methods for the application of these models using commercial conventional reservoir models to predict well productivity are developed. This provides a valuable tool in estimating the productivity and economic value of a potential </span></span></span>shale gas play where only geological, petrophysical and geo-mechanical and limited or analogue production data is available.</p></div>","PeriodicalId":101264,"journal":{"name":"Upstream Oil and Gas Technology","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.upstre.2020.100001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136717476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}