Constrained, Multi-Objective, Steamflood Injection Redistribution Optimization, using a Cloud-Distributed, Metamodel-Assisted, Memetic Optimization Algorithm

Abstract

AbstractA cloud-distributed optimization algorithm applicable to large scale, constrained, multiobjective, optimization problems, such as steamflood redistribution, is presented. The proposed algorithm utilizes the so-called Metamodel Assisted Evolutionary Algorithm (MAEA) as its algorithmic basis. MAEAs use a generic implementation of an evolutionary algorithm as their main optimization engine and advanced machine learning techniques as metamodels. Metamodels are utilized through the application of an inexact pre-evaluation phase during the optimization, which substantially decreases the evaluations of the problem specific forward model. Additionally, a unification of global search (GS) and local search (LS) is achieved via the use of Lamarckian learning principles applied on top of a MAEA creating, in essence, a Metamodel Assisted Memetic Algorithm (MAMA). MAMAs profit from the abilities of MAEAs to explore the most promising regions of the design space without being trapped in local optima while also utilizing the efficiency of deterministic methods to further refine promising solutions located during GS. At the end of each EA generation, the most promising members of the current populations are selected to undergo LS using a gradient-based method. Further, integration with scalable cloud-distributed computing allows MAMAs (CD-MAMA) to perform rapid and simultaneous evaluation of tens of thousands of operating plans.The proposed algorithm has been statistically validated on two mathematical test cases and, subsequently, used to optimize a field undergoing steamflood under two different oil-price scenarios. Thus, demonstrating that, cloud-distributed MAMAs coupled with efficient reservoir models, allow for steamflood injection redistribution optimization in affordable, by industry, wallclock times (hours). For the field in question production comes from poorly consolidated sands within the Antelope Shale member of the Miocene Monterey formation with porosity averaging 30%, permeability averaging 2,000 mD and net thicknesses typically between 50 and 300 feet. Structural dip is steep at approximately 60 degrees. The reservoirs are shallow, with depths ranging from 200 - 600 feet TVD. Oil gravity is approximately 13° API. Reservoir pressures are well below bubble point and average 50 - 100 PSI. The field has about 200 producers and 30 injectors, producing about 2000 bpd of oil and injecting 8000bpd of steam. The field has been under operation for about 7 years, with most of the continuous steamflood starting about 3.5 years ago.