Data-Driven Technologies Accelerate Planning for Mature-Field Rejuvenation
- Adam Wilson (JPT Special Publications Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- January 2018
- Document Type
- Journal Paper
- 54 - 57
- 2017. Society of Petroleum Engineers
- 3 in the last 30 days
- 138 since 2007
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This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 185751, “Using Data-Driven Technologies To Accelerate the Field-Development-Planning Process for Mature-Field Rejuvenation,” by Jeremy B. Brown, SPE, Amir Salehi, SPE, Wassim Benhallam, SPE, and Sebastien F. Matringe, SPE, Quantum Reservoir Impact International, prepared for the 2017 SPE Western Regional Meeting, Bakersfield, California, USA, 23–27 April. The paper has not been peer reviewed.
This paper presents a data-driven technology and associated work flow for fast identification of field-development opportunities in mature oil fields that accelerates subsurface field-development planning and reduces the time requirement from months to weeks. This technology is ideally suited for large, complex oil fields with large data sets and has been used for brownfield rejuvenation, for asset evaluation during acquisition, and as an independent validation system within internal review programs for large oil companies.
This approach does not require fullfield static or dynamic modeling but retains a high degree of confidence by coupling a reliance on data and analytics with judgment of experienced subject-matter experts who quickly validate results. This methodology relies on automated or machine-assisted geological and engineering work flows. Because the forecasting algorithms use regression or neural-network techniques that train on historical data, this approach is not applicable to undeveloped or early-stage greenfields.
This technology relies on rapid integration and quality control of key geological and well data, including wireline logs, production/injection by well, completions data, and well trajectories. Automated, machine-assisted, or neural-network-driven work flows enable quick geological mapping, estimation of flow contribution by stratigraphic flow unit, decline-curve analysis, single and multitank material-balance history matching, fractional-flow modeling, drainage-area estimation for all wells by flow unit, identification of pay behind pipe, identification of unswept/bypassed-oil pockets, production mapping using numerical tracers, and waterflood optimization.
Reservoir-Engineering Work Flows
The first step of this approach is rapid analysis of production, injection, and pressure data. Numerous standard work flows of reservoir engineering are auto mated or machine-assisted during this phase.
Performance Summary of Active Wells. Maps and trends of oil, gas, and water production and well vintage and grouping analyses by layer and block provide a quick, automated framing analysis of reservoir trends.
Offline-Well Analysis. Special emphasis is given to wells currently offline, which often present the quickest and easiest opportunities to raise production. The cause of each well’s shut-in is characterized on the basis of trends in water cut, gas/oil ratio, and pressure. These analyses will lead to specific opportunities to restore production from offline wells.
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