I. Jamaludin, Zhaoyuan Tan, Nicholas Aloysius Surin, Zhong Ying Hew
{"title":"Improving Aging Field Value Through Production Data Revitalization","authors":"I. Jamaludin, Zhaoyuan Tan, Nicholas Aloysius Surin, Zhong Ying Hew","doi":"10.4043/31524-ms","DOIUrl":null,"url":null,"abstract":"\n Field-D consists of multi-stacked oil reservoirs with commingle production and dual strings. Well integrity issues are main challenges causing crossflow between zones and unintentional production/injection. With massive effort of rebuilding static model and rework on history matching (HM), many inconsistencies of production data allocation were discovered, such as oil recovery factor (RF) >60%. This paper discusses the exercise of cleaning up production data as per latest subsurface understanding and memory production logging tool (MPLT) campaigns.\n Reinterpretation of stratigraphic correlation, depositional environment, and petrophysical parameters are among major workflows during the static model rebuilding process. As current reservoir allocations are based on legacy permeability-thickness (KH) ratio, the requirement to revise allocation split based on latest understanding is considered compulsory. This is further justified by dynamic modeling HM exercise where inconsistencies are observed between legacy data and model response, resulting in poor HM quality and unrealistic RF, suggesting different production/injection distribution than in current database. Additionally, MPLT campaign is executed every year to obtain latest split percentage. A pilot exercise is embarked on to redefine contribution for each zone in every well in fault block VIII and rerun allocation starting day 1.\n Successful production data allocation rerun is expected to demonstrate representative RF for individual reservoir. Higher RF are expected for zones with secondary drive mechanism i.e., water and gas injection, as opposed to natural depletion reservoirs (NDR). This exercise also will enable accurate reporting during the Annual Reporting of Petroleum Resources (ARPR) to the host government. Additionally, more accurate forecast using decline curve analysis (DCA) can be realized, and robust analysis can be performed to improve respective reservoir RF. Better well management also is anticipated as zonal isolation such as water or gas shut off or adding perforation jobs can be properly planned and executed. Looking from injection well point of view, proper distribution will be possible so that only targeted zones will receive the pressure support. A full field implementation is currently ongoing for the rest of fault block in Field-D.","PeriodicalId":11081,"journal":{"name":"Day 2 Wed, March 23, 2022","volume":"42 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Wed, March 23, 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4043/31524-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
Field-D consists of multi-stacked oil reservoirs with commingle production and dual strings. Well integrity issues are main challenges causing crossflow between zones and unintentional production/injection. With massive effort of rebuilding static model and rework on history matching (HM), many inconsistencies of production data allocation were discovered, such as oil recovery factor (RF) >60%. This paper discusses the exercise of cleaning up production data as per latest subsurface understanding and memory production logging tool (MPLT) campaigns.
Reinterpretation of stratigraphic correlation, depositional environment, and petrophysical parameters are among major workflows during the static model rebuilding process. As current reservoir allocations are based on legacy permeability-thickness (KH) ratio, the requirement to revise allocation split based on latest understanding is considered compulsory. This is further justified by dynamic modeling HM exercise where inconsistencies are observed between legacy data and model response, resulting in poor HM quality and unrealistic RF, suggesting different production/injection distribution than in current database. Additionally, MPLT campaign is executed every year to obtain latest split percentage. A pilot exercise is embarked on to redefine contribution for each zone in every well in fault block VIII and rerun allocation starting day 1.
Successful production data allocation rerun is expected to demonstrate representative RF for individual reservoir. Higher RF are expected for zones with secondary drive mechanism i.e., water and gas injection, as opposed to natural depletion reservoirs (NDR). This exercise also will enable accurate reporting during the Annual Reporting of Petroleum Resources (ARPR) to the host government. Additionally, more accurate forecast using decline curve analysis (DCA) can be realized, and robust analysis can be performed to improve respective reservoir RF. Better well management also is anticipated as zonal isolation such as water or gas shut off or adding perforation jobs can be properly planned and executed. Looking from injection well point of view, proper distribution will be possible so that only targeted zones will receive the pressure support. A full field implementation is currently ongoing for the rest of fault block in Field-D.