Application Of Learning Curve Models To Oil And Gas Well Drilling

Abstract

Abstract It is a fundamental human characteristic that a person engaged in a repetitive task will improve his person engaged in a repetitive task will improve his performance. Attempts to quantify this improvement performance. Attempts to quantify this improvement gave rise to the concept of manufacturing progress function commonly called the learning curve. The learning curve is used consistently in many industries as a basis for projecting costs, forecasting manpower requirements, rating personnel, scheduling production, and negotiating multimillion dollar contracts. In oil and gas well drilling, the first well drilled in a new field usually takes the most time. A progressive reduction in drilling time is obtained with subsequent wells until a point is reached when there is no significant improvement. This paper analyzes the improvement obtained from drilling experience using the learning-curve technique. Results from the analysis are used to predict drilling costs, drilling time, and manpower requirement for subsequent drilling operations. Introduction The success of any commercial venture depends upon the ability to make profit. Profit might be taken simply as the difference between selling price and all costs. Thus, if we had a perfect ability all times to forecast our prices and our costs had some degree of certainty about our volume, could calculate our profits quite readily, and business decision-making would be quite easy. It is a fundamental human characteristic that a person engaged in a repetitive task will improve his person engaged in a repetitive task will improve his performance. The first attempt to quantify this performance. The first attempt to quantify this improvement was made in the aircraft industry. This gave rise to the concept of manufacturing progress function commonly called the learning curve. The most common factors that give rise to performance improvement as cumulative output increases include the following:job familiarization by the workmen,improvement in tool coordination, shop organization, and engineering liaison,development of better methods, anddevelopment of better and more efficient tools. This indicates that not only does the worker improve, but management in turn becomes more efficient through repetitive tasks. In the petroleum industry, the first oil or gas well drilled in a new field usually takes the most time. As the operator becomes more familiar with the area, he learns to tailor his mud and drilling programs to the area, and obtains a progressive programs to the area, and obtains a progressive reduction in drilling time until a point is reached when there is no significant improvement. Also, footage cost for drilling oil and gas wells has been decreasing over the years because of improved drilling technology and the optimization of drilling control. This paper analyzes the improvement obtained from drilling experience in the petroleum industry using the learning-curve technique. Examples are given on the use of the learning-curve technique to predict costs, drilling time, and manpower requirement for subsequent drilling operations. THE LEARNING CURVE Learning patterns reportedly were first observed for manufacturing operations in 1925 in Ohio. During subsequent years, definitive studies of aircraft assembly showed the following pattern: the fourth plane required only 80 percent as much direct labor as the second; the eighth plane, only 80 percent as much as the fourth; the 100th, only 80 percent as much as the fourth; the 100th, only 80 percent as much as the 50th; and so on. Thus, the percent as much as the 50th; and so on. Thus, the rate of learning to assemble aircraft was concluded to be 80 percent between doubled quantities. The characteristic learning curve is shown on Cartesian coordinates in Fig. 1 and on double logarithmic coordinates in Fig. 2. On am arithmetic chart, with linear coordinates, the relationship is a curve showing a rapid initial decline that later trails off. On a log-log chart, however, it is a straight declining line, which reflects a constant rate of reduction. Such a straight-line relationship is easier to draw and use for prediction purposes.