A Modified Genetic Algorithm for Horizontal Well Placement Optimization in Gas Condensate Reservoirs

Abstract

Determining optimum well locations is a crucial step in field development. Often, broad possibilities and constrains on computational resources limit the scenarios that can be considered. When dealing with heterogeneities, the intuitive engineering judgment may not be sufficient, and use of optimization algorithms becomes necessary in finding a favorable production plan. Although there have been extensive publications on optimization algorithms for oil reservoirs, there is little research aimed at gas or gas condensate reservoirs. In this work, we studied optimization of horizontal well placement in gas condensate reservoirs. One of the current challenges in development of gas condensate fields is condensate blockage. As pressure decreases condensate accumulates around the wellbore, leading to significant reduction in gas production. Horizontal wells can effectively solve this problem. However, due to their high cost, and complex phase behavior determining optimum locations cannot be based only on intuitive judgments. Here we present a horizontal well-placement optimization method for gas condensate reservoirs based on a modified genetic algorithm. Unlike oil reservoirs, the cumulative production in gas reservoirs does not vary significantly (although the variation is not economically negligible) and there are possibly more local optimums. Therefore the possibility of finding better production scenarios in subsequent optimization steps is not much higher than the worse case scenarios, which delays finding the best production plan. In this work, we use a cumulative distribution function to magnify the difference between production scenarios. As a result of this change we were able to find the best scenario with considerably fewer simulations. We apply the modified algorithm to a section of Qatar's North Field, a gas condensate field with the world's largest gas reserves. Our results show that this method can efficiently find the optimum horizontal well locations and can lead to valuable increase in gas and condensate production.

Development of an efficient hybrid GA-PSO approach applicable for well placement optimization

“…Emeric et al (2009) performed an integrated study based on GA to optimize the number, location, and trajectory of different deviated production and injection wells with nonlinear constraints. Morales et al (2010) introduced modified GA for optimizing the location of horizontal wells in gas condensate reservoirs considering different parameters. They proposed a modification which allows user-defined level of risk to be integrated into the optimization scheme to find the optimum well locations.…”

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

“…Based on the observation that the variation in cumulative production in gas or gas-condensate fields is not very sensitive to well location, Morales et al (2010b) implemented a modification in the genetic algorithm that resulted in faster convergence to the optimum location compared to a conventional genetic algorithm.…”

A Closer Look At Differential Evolution For The Optimal Well Placement Problem

“…Another important advantage of applying meta-heuristics is that the number of function evaluations is dependent on the size of the population and not the problem dimensionality [33]. Meta-heuristics that have been used to solve the well-placement problem include: genetic algorithms (GA) [35,24,21], simulated annealing (SA) [3], particle swarm optimization (PSO) [28,17,1,16], covariance matrix adaptation evolutionary strategies (CMA-ES) [12,5,14], and differential evolution (DE) [27,2].…”