ISSN: 2578-4846
Authors: Akintola SA* and Olawoyin AB
Drilling operations in the oil and gas industry takes most of the well cost and how fast the drilling bit penetrate and bore the formation is termed the Rate of penetration (ROP). Since most of the cost incurred during drilling is related to the drilling operations, there is need not only to drill carefully, but also to optimize the drilling process. A lot of parameters are related to the rate of penetration which are actually interdependent on each other. This makes it difficult to predict the influence of every single parameter Drilling optimization techniques have been used recently to reduce drilling operation costs. There are different approaches to optimizing the cost of drilling oil and gas wells, some of which include static and /or real time optimization of drilling parameters. A potential area for optimization of drilling cost is through bit run in the well but this is particularly difficult due to its significance in both drilling time and bit cost. In this sense, as a particular bit gets used, it gets dull as its footage increases, resulting from the reduction in the bit penetration rate. The reduction in penetration rate increases total drill time. In order to optimize bit cost, it is desirable to find a trade-off between the two by a bit change policy This study is aimed at minimizing drilling time by use of artificial intelligent for the bit program. Data obtained from a well in the Niger delta region of Nigeria was used in this study and the cost optimization modelled as a Markov decision process where the intelligent agent was to learn the optimal timings for bit change by reinforcement policy Iteration learning. This study was able to achieve its objectives as the reinforcement learning optimization process performed very well with time as the computer agent was able to figure out how to improve drilling cost over time. Better results could be obtained with a better hardware and increased training time.
Keywords: Artificial Intelligence; Drilling cost; Drilling parameters; Markov decision; Rate or penetration
