End-to-End Approach Enhances Value of Mature and Marginal Fields

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 222165, “Optimization Well Targeting, Platform Placement, and Path Refinement to Enhance Mature and Marginal Field Values,” by Laainam Chaipornkaew, PTTEP; C. Pipatchatchawal, The Gang Technology Company; and S. Kosawantana, PTTEP, et al. The paper has not been peer-reviewed. _ Enhancing hydrocarbon recovery from compartmentalized reservoirs in the Gulf of Thailand (GOT) requires many production wells. Regional operators have enhanced their operational expertise by adopting a statistical drilling approach. This strategy involves multistack targeting and commingled production plans, aiming to increase hydrocarbon encounters by drilling near faults and within statistically inferred intervals indicating new sands. The complete paper focuses on enhancing the operator’s in-house workflow into a complete end-to-end solution. Two key focus areas are algorithm improvement and plug-in development. These solutions are now applicable to conceptual field development plans (FDP) in both exploration and development projects. Methodology The methodology consists of the algorithm and the plugin. The algorithm features three key steps, plus a preconditioning step to automate fault polygon preparation in Step 0. The integer-linear-programming algorithm identifies optimal targets in Step 1 and determines optimal wellhead platform (WHP) locations in Step 2. After one WHP scenario is selected, Step 3 refines paths to maximize the number of reachable wells. Designed for ease of use by geoscientists, the plug-ins offer an interface for the inputs to be selected and formatted easily and for the output to be visualized with commercial subsurface software. While the algorithm handles computation, plug-ins manage data exchange and handling. The described development effort concentrates on automation and optimization while leveraging data preparation, 3D visualization, and revision capabilities available in commercial platforms. Algorithm Development. Step 0: Data Preparation With Preconditioning. The optimization workflow requires data similar to that used in typical manual-target and WHP planning. The algorithm optimizes available space based on geometry, necessitating that all data be in the same frame of reference. The shape of fault polygons is crucial for determining areas of interest (AOI), but generating accurate shapes can be challenging because of poor input quality. Polygons are extracted automatically from intersections between fault and horizon surfaces within allowable tolerances. The authors’ initial approach used a computational geometry method known as “alpha shape” to fit unorganized points into polygons. However, the fault-polygon-extraction algorithm was enhanced by using a geologically meaningful constraint. This simple trigonometric relationship was then used to govern fault-polygon thickness, shrinking directly from the wider fault zone. This method allows for larger tolerances during the intersection search because the thickness can be easily corrected without missing the fault polygons.