Field Surface Planner Algorithm for Integrated Gas Asset Management

As the world transitions towards a more sustainable energy landscape, the need for a less environmentally harmful transition fuel becomes increasingly crucial. This has led to renewed interest in developing previously abandoned natural gas fields. In this paper, we present Field Surface Planner Algorithm (FSPA), a DCA-based model implemented in Python, which aims to generate an effective investment timing strategy for bringing new wells online in large gas fields. The model considers various surface facilities and commercial constraints, while prioritizing the fulfillment of contractual gas demand. FSPA's algorithm revolves around a sales error concept, which quantifies the disparity between the current sales gas rate and the nominated gas demand. By comparing this sales error against a predetermined error tolerance, the model determines the optimal timing for introducing new wells into production. This approach ensures that the gas field's total sales gas rate remains in line with the nominated gas demand, while minimizing any potential environmental impact. To ensure efficient operation, FSPA operates within the bounds of nominated gas demand constraints and facility constraints. Nominated gas demand constraints serve as a limit against which the on-stream dates and DCA forecasts for future wells are optimized, minimizing the need for flaring gas. Facility constraints restrict the volume of gas supply that the algorithm optimizes, aligning it with the capabilities of the surface facilities. During a well's initial contribution, FSPA generates accurate DCA forecasts based on several key factors, including the exact amount of sales gas required to close existing gaps, the well's reserves, and economic limitations. This information aids in making informed decisions regarding short-term production shortfalls and facilitates effective negotiation with customers, ultimately delivering substantial value for the organization involved in gas field development. By providing an efficient solution for well sequencing, the proposed model significantly reduces gas flaring costs, minimizes opportunity costs associated with production shortfalls, and assists in short-term decision-making processes. Overall, it offers a valuable contribution to sustainable energy transition efforts and promotes responsible development in the gas industry.