Abstract
An efficient approach to obtain the optimum design under uncertainty for a wide range of reservoir simulation applications has been developed and successfully implemented.The approach discussed here significantly reduces the time required to evaluate optimum designs for improved oil recovery (IOR) processes.
Determining the optimum combination of design variables for an IOR process is a complex problem that depends on the crude oil price, reservoir and fluid properties, process performance, and well specifications.Due to the large number of design variables, numerical simulation is often the most appropriate tool to evaluate the feasibility of such a process.However, because of the economical and geological uncertainties, the optimum design should be expressed as a distribution to gauge the uncertainties.
Our innovative simulation approach has the capability to determine an economically optimum design that includes the following variables for surfactant/polymer flooding projects.
The duration of water injection prior to the surfactant flooding
Surfactant concentration and slug size
Polymer concentration injected with the surfactant
The concentration and duration of the polymer drive
The salt concentration in different stages of the flood
The uncertain parameters considered in this study were Dykstra-Parsons coefficient as a measure formation heterogeneity, average reservoir permeability, horizontal correlation length, ratio of horizontal to vertical correlation lengths, vertical to horizontal permeability ratio, residual oil saturation, surfactant adsorption, price of crude oil and chemicals, and discount rate.
In order to efficiently perform these complex design processes efficiently, a platform that distributes multiple simulations onto a cluster of computer processors has been developed. The platform integrates several oil reservoir simulators, an economic model, an experimental design and response surface methodology, and a Monte Carlo algorithm with a global optimization search engine to identify the optimum design under conditions of uncertainty.
The technique incorporates the following steps:
Factorial design to find the most influential design and uncertain factors.
Response surface methodology (RSM) design over those most influential factors to fit a response surface using net present value (NPV) as the objective function.
Monte Carlo simulation over the response surface to maximize the mean of the net present value and search for the optimum combination of the design variables at the same time.
This approach is applied to a field-scale surfactant/polymer flood using the UTCHEM simulator to find the optimal values of design variables that will maximize the NPV.
