ABSTRACT
Determination of optimal well locations for infill drilling is a
challenging task because engineering and geologic variables affecting
reservoir performance are often nonlinearly correlated and have some
degree of uncertainty attached to them. Numerical models which are the
basis of well placement decisions rely on data that are uncertain, which
in turn translate to uncertainty in our numerical simulation forecasts.
The objective of this research is to employ an efficient optimization
technique to the well placement problem to determine the optimum infill
well location. Based on the success of its previous application by
other authors in solving the well placement problems, Genetic Algorithm
(GA) will be used here as the main optimization engine. An experimental
design is used to generate some experimental simulation runs using the
uncertain parameters, and these uncertain parameters are used to fit a
response surface model of the objective function. The response surface
methodology is used to identify the optimum design under conditions of
uncertainty to build a proxy model that can be utilized to predict the
cumulative oil produced.
Our application of GA to determine the optimal location for infill
well placement in a synthetic reservoir is improved by using a set of
screening criteria and some engineering judgment to reduce the search
space for possible locations. The proxy model generated from the
response surface methodology is also combined with GA to determine the
optimal locations for three cases of drilling two, four or six
additional infill wells in the reservoir modeled in this study.
The study found that response surface models can be used as a proxy
tool coupled with GA to provide reliable results; and to reduce the
number of simulation runs required for the well placement optimization
problem.
CHAPTER 1
INTRODUCTION AND STATEMENT OF THE PROBLEM
1.1 Introduction
There is a growing demand to develop petroleum reservoirs through the
drilling of in-fill wells to exploit the hydrocarbon reserves not
properly drained by existing producing wells. Well placement can be
referred to as all activities associated with drilling a wellbore to
intercept one or more specified locations. The term is usually used in
reference to vertical, directional or horizontal wells that are oriented
to maximize contact with the most productive parts of reservoirs. As
well spacing is decreased, the shifting well patterns alter the
formation-fluid flow paths and increase sweep to areas where greater
hydrocarbon saturations exist. A wide well spacing will leave some oil
and gas bearing sands in areas not penetrated, while a close spacing
will cause some oil and gas bearing sands to be penetrated by two wells
or more, causing interference and lowering the reserves drained by the
wells and economic profit. This study is done to determine the optimal
locations for well placement to support field development plans.
One of the most challenging and influential problems associated with
drilling in-fill wells is finding the optimum number of wells and their
placement in the reservoir. In this problem, there are many variables to
consider like geological, well configurations, production variables and
economic variables. All these variables, together with reservoir
geological uncertainty, make the determination of a suitable development
plan for a given field difficult, since the design has to evaluate
hundreds or thousands of potential infill alternatives.
The task of optimization of infill well placement is challenging,
because the evaluation of the production capacity of many wells may be
required, with each evaluation requiring the performance of a simulation
run; and for large or complicated reservoir models, the simulation run
time can be excessive. The number of simulations required depends on the
number of optimization variables, the size of the search space, and on
the type of optimization algorithm employed.
Different optimization methods can be used to determine the optimum
well locations in a reservoir. This optimization problem is nonlinear
and generally contains multiple local minima. Gradient-free optimization
algorithms are commonly used for well placement problems because of
their computational efficiency. Genetic Algorithm (GA) is one of the
Gradient-free optimization methods used in the industry. GA will be used
as the main optimization engine in this work because of its success
application by several authors in solving complex optimization problems
with high dimensionality and nonlinearity. The main focus of this work
is to employ an efficient optimization technique for in-fill well
placement and optimization; i.e., to determine the best possible
locations of infill wells for optimal development of a field. We intend
to identify the significant parameters that affect well placement in the
reservoir and use some screening parameters to identify potential
locations for well placement. The use of Experimental Design (ED) and
Response Surface Methodology (RSM) has been shown to be effective tools
for uncertainty analysis. They were utilized in this work to consider a
range of values for the controlling parameters and to build a proxy
model that can be used to predict the objective function. Experimental
design methodology offers not only an efficient way of assessing
uncertainties by providing inference with minimum number of simulations,
but also can identify the key parameters governing uncertainty in
production forecast.