BHPSO combined with statistical net hydrocarbon thickness map for well placement optimization under uncertainty

摘要

Investments in oil and gas projects are driven by critical field development decisions including well placement, which often significantly affect the projects' economics. Due to their typically high cost and inherently insufficient data (especially in greenfields, or fields in their early stage of development), managing uncertainty is critical when optimizing a field development plan. The use of a single deterministic base case for hydrocarbon, both in place assessment and production forecasting is often misleading and leads to sub-optimal decisions. Consequently, robust field development plans require multiple geological realizations covering the range of uncertainty in reservoir properties and encompassing both multiple geological concepts and geostatistical properties distribution. Typically, an objective function such as the average net present value (NPV) or the average cumulative oil production (COP) is optimized in order to select an optimal development scenario. Nevertheless, such an assessment can be computationally prohibitive, especially when using optimization methods require hundreds, often thousands of costly simulations over a single realization, a number that significantly increases when multiple realizations are involved. This study proposes a new method for well placement optimization under uncertainty, building on map-based evolutionary optimization technique: the black hole particle swarm optimization (BHPSO). The statistical net hydrocarbon thickness (SNHCT) map is introduced to guide the BHPSO algorithm; and hence, pragmatically account for uncertainty in the process of well placement optimization. We optimize well placement on the realization corresponding to the minimum difference between its NHCT map and the SNHCT map. The SNHCT combines the average and the P90 NHCT maps; hence, assuring that the selected sweet spots for well placement are statistically the best with regard to the multiple subsurface realizations. The method is applied on the Olympus benchmark case and results are compared to two scenario reduction methods: RMfinder and k-means-k-medoids Clustering. Results show superior performance over the two methods in terms of optimality of the result and the required computational load.