Optimal Field Development Planning of Well Locations With Reservoir Uncertainty

Abstract

The paper presents a process for determining optimal subsurface locations for producing and injecting wells in a field to improve reservoir performance and value. The process is illustrated by optimizing well locations in an initial development planning context. The process involves planning a set of wells on a static reservoir model using an automated well planner. These locations are then optimized with dynamic flow simulation to achieve higher recovery or economic benefit. In this work, we present the framework for optimizing many well locations simultaneously, with reservoir simulation and economic analysis. This method is demonstrated byapplying it to a field-scale simulation model with water injection. Introduction Forecasting optimal number, type, subsurface locations, and design parameters for a new set of wells, considering field uncertainty, is a complex and often a time-consuming set of challenges for field development planning. But it is a necessary and critical part of the field development planning workflow. Sub-optimal decisions on the number of wells, the size and configuration of platforms, the processing capacity of facilities, etc which are made early in the field life may constrain field operations for years. The problem is often addressed through a tedious process of locating one well at a time in a static model, and then validating a set of well locations through case studies with reservoir simulation; then repeating this process until some convergence to a "good" set of wells is reached. With only a small number of cases investigated, there may be little confidence by an asset team that an overlooked alternative could be more attractive. Previous reseachers have proposed using mathematical optimization on this problem. All previous work recognized that the well location problem is a highly combinatorial, nonlinear optimization problem with integer variables. Early work experimented with integer programming[1,2] and ranking on static reservoir models[3]. Beckner and Song[4] used simulated annealing optimization with reservoir simulation, and Seifert et al[5] used exhaustive sampling and uncertainty analysis with simulation. More recent advances,[6,7,8,9] have shown that a genetic algorithm or a hybrid genetic algorithm coupled with flow simulation can be used to optimize and configure well plans. The more recent work demonstrated locating a small number of new wells or configuring a complex well, such as a multilateral well. The problem of locating many wells simultaneously when formulated as an optimization problemis that it can result in innumerable solution combinations. Thus, a practical procedure for locating many wells in a full-field development plan has been elusive. In this current work, we present a framework for optimizing many well locations with design constraints simultaneously. Rather than solve the full problem all at once, the method identifies a set of target and well plan locations based on the static reservoir model and then uses the locations to "seed" the global optimization as initial guesses. The method locates initial target perforation intervals using static reservoir properties, e.g., porosity, pore volume, or saturation, with a greedy-search algorithm.These locations are used as initial decision varaiables by the optimizer, which tunes the variables using a global, metaheuristic optimizer and flow simulation. The locations are risked, based on subsurface uncertainty, through analysis of the statistical character of the oil recovery or net present value within the optimization procedure. The mean recovery can be maximized with requirements on the statistical risk, e.g., the standard deviation. A key to the success of the optimization is efficiently running optimizer simulations on a computer cluster or grid. The paper discusses the architecture that enables efficient execution of hundreds or thousands of full-field simulations and the nature of the parallel optimizer We demonstrate the methodby applying it on a field-scale simulation model with water injection. We show that dynamic simulation was critical to improving well locations for this example.

Process systems engineering perspective on the planning and development of oil fields

“… Schematic of a multireservoir hydrocarbon field with different types of wells and surface infrastructure. (Reprinted with permission from Ref , Copyright 2016 American Chemical Society. )…”

“… Oil production level and number of wells for the past 46 years in the Trapp field (Kansas). (Reprinted with permission from Ref , Copyright 2016 American Chemical Society. )…”

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

“…Some researchers suggested the use of parallel computing to distribute the simulations on a network of computers (e.g. Cullick et al (2005), Bangerth et al (2005), Parashar et al (2005)). Other researchers combined finite difference with various proxy models or response surface models (RSM) to reduce necessary reservoir simulations (e.g.…”

“…Another popular approach in handling uncertainty in well placement is using Monte Carlo simulation (e.g. Cullick et al (2003Cullick et al ( , 2005). Monte Carlo simulation uses a probability distribution for the uncertain input parameters.…”

Coupled Surface/Subsurface Simulation of an Offshore K2 Field

“…In uncertainty mode, the Optimizer intelligently samples from the userdefined variable set according to one of a number of sampling techniques such as Experimental Design and Latin Hypercube and runs a series of predictions in order to produce a thorough assessment of cause-and-effect. Details and applications of the optimization method have been discussed in literature (Saputelli et al 2009;Cullick et al 2005;Cullick et al, 2007).…”