Utilizing Bayesian Optimization and Machine Learning to Find the Best Inflow Control Design for Horizontal Wells

Abstract

Inflow Control Devices (ICDs) help reduce the adverse consequences of uneven inflow issues in a lateral completion system. The most common uneven inflow consequences are early water breakthrough and gas coning in water-driven and saturated reservoirs. These issues lead to the dominance of undesired fluid production and consequently, reduced well productivity. Typically, uneven inflow issues are caused by different drivers, including heterogenous permeability, an uneven water saturation profile, and/or complex well completion in a lateral section of a given well. ICDs are placed in permanent positions along the lateral section of a well in order to control zonal production and improve well productivity. The goal of utilizing ICDs is to delay water or gas production and equalize the inflow production from the reservoir to wellbore. However, the uncertainty of reservoir characteristics and operational constraints add complexity to the ICD design and complicate optimization strategies. An optimum ICD design entails identifying the number and size of compartments, packer locations, ICD type, and number of ICDs in each compartment, and the ICD settings such as orifice diameter or flow restriction rating. Extensive reservoir modeling work can be performed to accurately quantify the impact of each ICD design on well production. The intent of this paper is to demonstrate that Bayesian optimization and machine learning techniques can help identify an optimized ICD design in a minimum number of reservoir simulation evaluations. These techniques are implemented into the reservoir simulation workflow to enhance the speed of the analysis and resulting value proposition for the operating customer.Using Gaussian Process Regression as a surrogate, Bayesian optimization makes use of a small number of initial reservoir simulation runs to quantify the uncertainty of the surrogate model in the parameter space. It makes use of an appropriate acquisition function (as determined by the desired exploration-exploitation tradeoff characteristics) to design the next sample (simulation run) to be evaluated. Unlike the ensemble-based optimization algorithms, Bayesian optimization points to the optimum solution sequentially (one evaluation at a time). The proposed workflow automates the optimization process of ICD design evaluation workflow times by 50% in our case studies. The 50% efficiency takes in the time to perform ICD optimization workflow. For instance, the manual iteration ICD design for case study 1 described in this paper was four weeks, which the proposed workflow shortened this time to two weeks.This paper presents two case studies in which the Bayesian optimization technique was used to identify the best ICD completion design. The space parameter in both case studies involves several variables, including the number and location of compartments, the number of ICDs per compartment, and the ICD settings (one such setting, for example, considers orifice diameter size). The goal in the first case study was to find an ICD design that can maximize the net present value over the well lifetime (set to 5 years), while reducing and delaying water production. In this first case study, an 800ft lateral in a horizontal well, with drastic variation of permeability along its lateral length, was considered. In the second case study, 4000ft horizontal length of a well with variations of permeability was analyzed. In this second case, the objective was to extend the life of the well by minimizing the gas-oil ratio and maximizing the oil recovery. The simulation runs stopped after 3 years of production and the best case was chosen based on the aforementioned criteria. In both case studies, the optimization algorithm setup was able to converge to an optimum ICD design within 20 reservoir simulation runs. This alone represents an improvement over the current manual trial and error process in which an expert uses human intuition.