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.

GET THE COMPLETE PROJECT»

Do you need help? Talk to us right now: (+234) 8111770269, 08111770269 (Call/WhatsApp). Email: [email protected]. Disclaimer: This PDF Material Content is Developed by the copyright owner to Serve as a RESEARCH GUIDE for Students to Conduct Academic Research. You are allowed to use the original PDF Research Material Guide you will receive in the following ways: 1. As a source for additional understanding of the project topic. 2. As a source for ideas for you own academic research work (if properly referenced). 3. For PROPER paraphrasing ( see your school definition of plagiarism and acceptable paraphrase). 4. Direct citing ( if referenced properly). Thank you so much for your respect for the authors copyright.

Related