The reservoir in discussion is a tight carbonate reservoir with low productivity and relatively under-developed albeit the huge in-place volumes. The expectation is that a detail reservoir characterization will provide insight on factors affecting reservoir productivity, spatial distribution of productive portion of the reservoir and offering solution to overcome reservoir tightness. The case study discusses on how a comprehensive multi-discipline review unravels and presents a robust reservoir heterogeneity framework. A geological review that includes both depositional and diagenetic process is performed to understand distinct components/factors responsible for reservoir heterogeneity. Simultaneously, petrophysical assessment was performed to quantitatively define rock grouping based on porosity-permeability, capillary pressure and pore throat distribution in the log and core domain. The multi-discipline observations were then reconciled to establish relationship between the process origin and the resultant product of specific group/range of reservoir petrophysical properties. The multitude of pore throat characters and its petrophysical properties were linked to the underlying geological processes. The established heterogeneity framework provides clarity on spatial distribution of the reservoir sweet-spot, factors controlling low productivity and the required mitigation. The study provides a complete journey of unlocking tight reservoir potential. It illustrates the geological studies influence toward innovative completion technology selection, design, and execution to overcome reservoir challenge. The study is supported by recent drilling and test results, hence offering insight for adoption and lesson learned.

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Data-Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide. A data-driven model is constructed for the first time for an oil field onshore Abu Dhabi, and used for evaluation of a reservoir with substantial reserves and comprehensive development plan; for the purpose of predicting production rates, dynamic reservoir pressure and water saturation, improving reservoir understanding, supporting field development optimization and identifying optimum infill well locations. The objective is to provide the asset with a decision-support tool to make better field development planning and management. The subject reservoir is a low permeability carbonate reservoir and characterized by lateral and vertical variations in its reservoir rocks and fluid properties. More than 8 years of Phase-I development and production/injection data and extensive amount of well tests and log data (SCAL, PVT, MDT) from more than 37 wells were used to construct the Data Driven Model for this asset. This new modeling technology, (TDM), integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. In this work, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance. Several analyses were performed using the full field data-driven model; complementing the existing conventional numerical model. The accomplishments of the data-driven reservoir model for this project included, but not limited to, comprehensive history matching (including blind validation) and then forecast of Oil rate, GOR, WC, reservoir pressure and water saturation, injection optimization, and choke size optimization. The results generated by the data-driven model proved to be quite eye-opening for the asset management; as the model was able to identify potential areas of improving field efficiency and cost reduction. When combined with numerical techniques, the calibrated data-driven model assist to obtain a reliable short term forecast in a shorter time and help make quick decisions on day-to-day operational optimization aspects. The use of facts (all field measurements) instead of human biases, pre-conceived notions, and gross approximations distinguishes data-driven modeling from other existing modeling technologies. Its innovative combination of Artificial Intelligence and Machine Learning (the technologies that are transforming all industries in the 21st century) with reservoir engineering, reservoir modeling and reservoir management clearly demonstrates the potentials that these pattern recognition technologies offer to the upstream oil and gas industry for its realistic digital transformation.