This paper describes an efficient approach to evaluate the water supply-to-injection cluster facilities capacity and also to define the required upgrades and areas of optimization.The supergiant filed understudy is located in Abu Dhabi and is producing for decades from multilayered carbonate reservoirs. The field is under peripheral water injection to maintain reservoir pressure and also to enhance the oil recovery.Total of 53 water injection clusters have been commissioned in the field to support water injection operation for reservoir management purpose. The water clusters consists of producing wells from water bearing formations and multiple injection wells completed in different reservoirs. Frequent down time in the water supply wells and existing bottle necks in the water supply to injection system has led Abu Dhabi Company for Onshore Operation to evaluate the clusters looping option to enhance the water injection capacity of the field and optimal re-distribution of the water through the clusters.By this strategy, the high capacity water supply wells will be able to feed the candidate clusters required extra water or the clusters with the closed water supply well under maintenance.To achieve this strategy, a fully integrated water supply to water injection system was built using a commercial fluid flow simulator. The integrated model consists of water supply well equipped with ESP, water injection facilities network with surface pumps, strainers and choke manifolds as well as injection wells. The system was validated against most reliable measured data at a snap shot of time.The full integrated water supply to injection model was used to evaluate the opportunities to loop the high capacity clusters to high demanding clusters, identify the bottlenecks in the system and also to determine the various options in the clusters facilities to enhance the water injection capacity of the field.
Although asset model based workflows are not new in digital oil field, drive towards automation is more and more increasing to move from periodic to continuous optimization for improving process and operational efficiencies. Integrated asset model forms the basis for number of well & reservoir workflows and can aid in standardization and automation. Models are heart of number of activities such as surveillance, calibration, optimization and forecasting within asset. These activities are inherent in most of workflows performed by engineers on any project or task. This paper is intended to discuss the best practices based on lessons learned from implementations in large mature brown fields in ADCO where sustaining allowable production, well performance issues, Production reconciliation, facilities bottle-necks & real-time data availability were major challenges. The corporate asset strategy shall have a vision towards automation and its benefits to organization's strategic objectives. Workflow automation for an asset will depend greatly depends on the objectives from a business process to accomplish and should be bringing maximum value. This must result in tangible impact whilst providing means to start establishing a new mindset. The initial efforts must focus on ‘fixing the basics’ such as mapping of existing detailed workflow steps of a process, identify key data required, thorough gap-analysis, improve data reporting & QA and agree on common definitions before automation takes place. Expectation setting with stakeholders should be done early in process and operations staff need to be involved early to help establish objectives and ensure workflows are adequate to their use. Prototype and phased workflow deployment approach shall be adopted. Engineers need to be given a chance to develop to trust automated system before workflows can be fully automated. Improving just process efficiency should not be end of goal of automation however engineers should be able to identify optimization opportunities in quick time. Automated model calibration can pinpoint data of poor quality and justify its improvement. Exception based well & facilities network surveillance is a common feature of automation hence rate estimation what if methodologies, validation limits, exception handling, pressure drop thresholds & pre-configuration of multiple operations scenarios shall be thoroughly discussed. Historical data trending in workflows can support decision making and add a value. Workflow and model governance need to be managed efficiently for automation to survive. Coherent and effective management information such as rolling-up of production volumes, deferment, operations KPIs need to be reported as a result of this automation to increase transparency. Agility, scalability and interoperability are key factors and must be supported by automation system. The authors primarily discuss challenges addressed in workflows deployment, data integration & improvements, capability development and change management mechanism in these implementations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.