Expansion of Data Analytics for Optimizing Steamflood In Mukhaizna Heavy Oil Field

Abstract

After successful implementation of data analytics for steamflood optimization at the Mukhaizna heavy oil field in Oman late 2018, Occidental expanded the project to two additional areas with a total of 626 wells in 2019, followed by full field coverage of more than 3,200 wells in 2020. In 2019, two separate low-fidelity proxy models were built to model the two pilot areas. The models were updated with more features to account for additional reservoir phenomena and a larger scope. On the proxy engine side, speed and robustness were improved, resulting in reduced CPU processing time and lower cost. Because of advancements in software programing and the pilots’ encouraging production performance, full-field coverage was accelerated so the model could support the efforts in optimizing steam injection during the 2020 OPEC+ production cut, not only to comply with allotted quotas, but also to allocate the resources optimally, especially the costly steam. Good improvements have been observed in overall steamflood performance, the models’ capabilities, and the optimization workflow. The steam/oil ratio has been reduced through the increase in oil production in both expanded study areas while keeping the total steam injection volume constant. Overall field steam utilization was improved both during the 2020 OPEC+ production cuts and during the production ramp-up stage afterward. With the continuous improvement in supporting tools and scripts, most of the steam optimization process steps were automated, from preparing, checking, and formatting input data to analyzing, validating, and visualizing the model outputs. Another result of these improvements was the development of a user-friendly web application to manage the model workflow efficiently. This web app greatly improved the process of case submittals, including data preparation and QC, running models (history matching and forecasting), as well as visualization of the entire workflow. In terms of optimization workflow, these improvements resulted in less time spent by the field optimization engineer in updating, refreshing, and generating new model recommendations. It also helped reduce the time spent by the reservoir management team (RMT) to test and validate the new ideas before field implementation. This paper will describe the improvements in the proxy model and the overall optimization process, show the observed oil production increases, and discuss the challenges faced and the lessons learned.

Data-driven modelling with coarse-grid network models

“…The family of interwell numerical simulation models (INSIM) focus on the dynamic behavior and interactions between multiple wells and use analytical or semi-analytical methods to evolve pressures and fluid compositions within each interwell connection [18][19][20][21][22]. A more general approach, referred to by different authors as StellNet/GPSNet/FlowNet, is to use standard finite-volume methods to represent the interwell connections and also allow direct fluid communication among different flow paths without going through the wells [23][24][25][26][27][28][29]. Compared with these interwell models, CGNet gives a richer network that has more free parameters per grid cell and a larger set of network paths connecting each pair of wells.…”