Data-Driven Analytics Provide Novel Approach to Performance Diagnosis

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 192759, “Data-Driven Analytics: A Novel Approach to Performance Diagnosis Using Spatiotemporal Analysis in a Giant Field Offshore Abu Dhabi,” by Mohamed Mehdi El Faidouzi, SPE, and Djamel Eddine Ouzzane, ADNOC, prepared for the 2018 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 12–15 November. The paper has not been peer reviewed. This work describes a heuristic approach combining mathematical modeling and associated data-driven work flows for estimating reservoir-pressure surfaces through space and time using measured data. This procedure has been implemented successfully in a giant offshore field with a complex history of active pressure management with water and gas. This practical work flow generates present-day pressure maps that can be used directly in reservoir management by locating poorly supported areas and planning for mitigation activities. Field Overview The giant oil field offshore Abu Dhabi covers an area of approximately 40×25•km. The structure is a low-relief anticline. The fault pattern is dominated by steep northwest/southeast strikes, with less-abundant northeast/southwest strikes. The fault throws are generally small, and most faults are unlikely to be sealing laterally. The oil accumulation is separated by dense argillaceous limestones into three distinct, stacked reservoirs (A, B, and C), each approximately 20 to 35 ft thick. Formation-pressure tests showed good intrareservoir vertical pressure communication. Occasionally, especially toward the north and east, a generally tight clay-prone lithology forms a localized barrier between the upper and basal layers of Reservoirs A and B. The complete paper examines the case of the upper layers of Reservoir A in particular, a laterally extensive porous carbonate deposited in a shallow-water environment, although the work flows developed are equally applicable to other reservoir zones. Commercial production of Reservoir A began in the late 1960s. After an initial period of natural depletion, various pressure-maintenance strategies were deployed, namely dump-flood (1972–1984), peripheral water injection (from 1979), and crestal gas injection (from 2005). Spatiotemporal Analysis of Pressure Data Prediction of spatial random fields is a common task in geostatistics and arises in geology, mining, hydrology, and atmospheric sciences. Kriging procedures are used routinely to make optimal predictions at unsampled locations. For quantities that vary in space and time, such as reservoir pressure, spatiotemporal interpolation can provide more-accurate predictions than purely spatial interpolation because observations at other times are considered. In producing oil and gas assets, reservoir-pressure measurements are made at a much higher frequency in the time domain compared with the spatial domain, which requires additional wells to be drilled. The conventional approach of using a spatial interpolation on the basis of a certain time period misses valuable information and could result in inconsistencies between maps from one time period to the next. In this case study, the authors present a heuristic approach to estimating reservoir pressure maps in Reservoir A on the basis of spatiotemporal interpolation. Reservoir pressure is modeled using smooth functions that capture global trends while preserving the spatial and temporal continuity of pressure and pressure gradient. The residuals can be described by a stationary and spatially isotropic process. Residuals then can be predicted at unsampled space and time locations by kriging.