Siliciclastic Reservoir Quality Model, a Multi-Criteria Decision Analysis approach for reservoir quality evaluation in the ‘OS’ field Niger Delta, Nigeria

Ayodele O. Falade , Olubola Abiola , John O. Amigun
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Abstract

This study presents a novel Multi-Criteria Decision Analysis (MCDA) model, the Siliciclastic Reservoir Quality Model (SRQM), for evaluating and ranking reservoirs in oilfields. The SRQM model integrates key reservoir properties, including net pay-to-gross ratio, porosity, water saturation, and shale content, to generate a comprehensive Reservoir Quality Index. The model was applied to the 'OS' field in the Niger Delta, Nigeria, and compared to the conventional Reservoir Quality Index (RQI) approach. The results show a weak negative correlation between the two methods (-0.05764), highlighting their complementary nature. The SRQM model offers a more comprehensive evaluation by incorporating both reservoir rock architecture (porosity and Vsh) and crucial fluid content (Sw and NTG), unlike RQI which focuses solely on rock architecture. SRQM revealed reservoirs 1 and 2 in well OS-5 as the highest quality reservoirs, with an SRQM index of 0.75 and RQI values exceeding 300. Furthermore, the SRQM model revealed variations within other reservoirs. For example, Reservoir 2 in well OS-1, identified as having excellent quality using SRQM, had a relatively low RQI due to its relatively low permeability. This indicates a trade-off between potentially larger hydrocarbon volumes and reduced porosity and permeability. While Reservoirs 1 and 2 have average RQI values of 225.27 and 227.57, indicating excellent quality compared to Reservoir 3 with an average RQI of 99.99, the SRQM ratings reveal a different ranking, with Reservoir 2 (SRQM index: 1.25) and Reservoir 3 (SRQM index: 1.8) considered higher quality than Reservoir 1 (SRQM index: 2.55). This study demonstrates SRQM's ability to consider multiple factors and provide a more robust approach to evaluating reservoir quality. This approach offers a significant improvement over conventional RQI methods, aiding in optimized reservoir development strategies.
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