Observational analysis found a 60% increase in productivity in Mauddud formation wells, highlighting the role of machine learning and fracture networks.
The primary objective of this paper is to introduce fit-for-purpose technologies and best practices aimed at increasing and sustaining the productivity of the Mauddud reservoir in the Greater Burgan Field, Kuwait, to a level of 40,000 bbl/day by 2030. Additional objectives include constructing DFN and 3D geomechanics models, and developing a mechanistic reservoir model to better understand reservoir fluid flow and select sweet spots for new horizontal infill wells. The approach involved a comprehensive analysis of the Mauddud Formation, characterized by thin layers, a tight carbonate matrix, and low productivity. The process included data gap analysis, updating the static model, and constructing 3D and 1D MEM geomechanics models. Natural fracture modeling was employed to understand the distribution of matrix permeability and fracture networks. Machine learning was used to propose development strategies, and cost-effective technology management programs were employed for drilling and workover candidate wells. The study reveals that natural fractures in the Mauddud formation are oriented NE-SW and are fault-related, formed due to multiple tectonic episodes (Fig. 2). The recovery mechanisms are flow from natural fractures and acid frac jobs supported by a larger volume of low-matrix permeability. Single porosity models were found to be inadequate to reproduce production behavior. Wells located within 500 meters of the main faults showed better productivity. Implementing multi-stage acid fracturing in two newly horizontal wells drilled based on study recommendations has increased current oil production by 60%. This paper presents novel insights into the Mauddud Formation's fracture orientation and recovery mechanisms, offering a new understanding of the reservoir's behavior. The use of advanced modeling techniques and machine learning to propose development strategies provides valuable contributions to the existing body of literature, benefiting the petroleum industry by enhancing reservoir management and production optimization.
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Al-Zankawi et al. (2025) studied this question.
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