Multiple-Objective Optimization Applied to Well Path Design under Geological Uncertainty

Abstract

Well placement and design under geological uncertainties defines a major risk for field development processes. Including alternative geological realizations in a manually optimized development plan has been cumbersome and time consuming. In practical cases, a single reference model was often used as a basis for the flow simulation model which included a major risk on underestimating geological uncertainties. Optimization approaches and related optimization methods applied in this field previously included an objective function definition which aggregates multiple objectives, e.g. cumulative well rates or economical indicators. These optimization methods do not allow distinguishing easily between correlated and uncorrelated objectives which defines a major limitation for efficiency. This paper presents a novel approach individually linking objectives and design parameters to multiple optimization processes which operate in parallel on a single simulation case. This approach increases the efficiency of stochastic optimization processes for complex application scenarios with multiple objectives. The extension to an uncertainty workflow including multiple geological models in the optimization processes is discussed.Well placement optimization of horizontal wells in independent target formations of the gas condensate field is used to verify the optimization methodology. Alternative geological models are included in the optimization workflow representing geological uncertainties. The specific objective of the case study is to find optimum well trajectories by maximizing cumulative gas production over the life cycle of the field. Performance indicators for all well candidates (design parameters) are related to individual objectives (regional production). Robustness criteria for well candidates are implicitly fulfilled on the basis of risk weighted geological realizations.

Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs

“…In this work, they use a Monte Carlo based sampling algorithm for screening purposes and a genetic algorithm for the optimization. Schulze-Riegert et al (2011) extended their previous study to the optimization of two horizontal well paths using a different optimization method (CMAES). In this work, they assumed three possible geological realizations with different weights.…”

Well Placement Optimization under Uncertainty with CMA-ES Using the Neighborhood

“…Although sharing this common similarity, the proposed approaches introduce different formulations of the objective function. In Schulze-Riegert et al (2010), Schulze-Riegert et al (2011), Onwunalu & Durlofsky (2010 and Chen (2010), the objective function is formulated as the expected value of the net present value over all the realizations, as shown in Eq. (1).…”

Multiobjective design and optimization of polymer flood performance

“…The genetic algorithm (GA) is also used to search the optimal location with fitness evaluated from multiple geologic models (Morales et al, 2011). The multiple realization approach has also been used for well-path design in gas-condensate field (Schulze-riegert et al, 2011). Alhuthali et al (2008) used streamlines to optimize production/injection rate.…”