Abstract
As resource owner and enabler, PETRONAS's Malaysia Petroleum Management (MPM) is entrusted to ensure maximizing recovery efforts from more than 1000 oil reservoirs under production in its portfolio. Performance and recovery from oil reservoirs depends on many factors that can be broadly classified into Reservoir Complexity and how the reservoir has been developed and managed. To undertake a development gap analysis and expectation setting the exercise was undertaken to benchmark reservoir performance against reservoirs of similar complexity. The objective was to take the learnings from better performing reservoirs and explore potential replication in poor performing reservoirs of similar complexity. The main challenge was to establish a single term to define Reservoir Complexity. This term should encompass all the factors like geological, petro physical, rock & fluid etc. that could potentially make the reservoir complex and at the same time also decide on the relative weightage of these parameters posing recovery challenges. Data analytics has been used to accomplish this task and the calibration with reference reservoirs has been achieved. This benchmarking tool can help to internally set targets for all fields where the recoveries have been lower than normally observed, help set EUR numbers for green fields and drive additional development strategies to maximize recoveries in existing fields where they are falling short.
When reservoirs of similar complexities are grouped together, they show varying performance indicators viz. recovery factor, decline rates etc. The gap analysis between the reservoirs of similar complexity has helped in identifying poor performing reservoirs and the underlying reasons for underperformance. Learnings from the better performing reservoirs have been incorporated and a detailed action plan has been prepared to improve the performance of these reservoirs. Considering the various ways in which this information can be used, a reservoir complexity benchmark would be a great asset to any major operator or regulator.
The workflow has been developed to calculate complexity based on the parameters that are affecting microscopic displacement efficiency, horizontal displacement efficiency and the vertical displacement efficiency. Data analytics has been used to assign weightages to each component posing recovery challenges and derivation of a single number defining complexity on a scale of 0 to 1. This is major improvement on all previous works of this nature attempted in various parts of the world and provides the user with not only the complexity per se but also its distribution. This benchmarking tool has been used for selected fields and has enabled development gap analysis and helped in initiating course correction to unlock more values from the underperforming reservoirs.
