Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems

For the last few decades multiple business sectors have been influenced by the advancement in Artificial Intelligence (AI). Though the oil and gas sector began to utilize the potential of AI comparatively latter than many other sectors, the appreciable amount of work has been done by researchers to equip the industry with AI tools. This work aims to explore various horizons of petroleum engineering by using different AI tools.;For providing better decision making in reservoir fluid characterization problem, fuzzy logic has been applied, which is an AI method to drive decisions when data is incomplete or unreliable. The second part of the work is the combination of supervised and unsupervised machine learning has provided an automated version of well log analysis, where the generated algorithm is able to distinguish between different lithological zones on the basis of well log parameters.;The majority of the problems such as drilling process optimization, production forecasting, comes under the umbrella of statistical regression. The supervised learning regression algorithm was generated to predict the drilling performance in terms of rate of penetration. The similar model was used for producing regression analysis of reservoir that has been treated by steam assisted gas drainage. The accuracy of both cases were investigated by comparing the prediction with available real time data.;The work has been concluded by providing conclusion gathered from comparing different methods and limitations of methodologies derived from Artificial Intelligent (AI) tools

Drilling Performance Monitoring and Optimization: A Data-driven Approach

Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and diferential pressures. The heavy hitter features identifcation and dimensionality reduction are performed to understand the impacts of each of the drilling parameters on ROP. This will be used to optimize the input parameters for model development and validation and performing the operation optimization when bit is underperforming. The model is frst developed based on the drilling experiments performed in the laboratory and then extended to feld applications. From both laboratory and feld test data provided, we have proved that the data-driven model built using multilayer perceptron technique can be successfully used for drilling performance monitoring and optimization, especially identifying the bit malfunction or failure, i.e., bit balling. We have shown that the ROP has complex relationship with other drilling variables which cannot be captured using conventional statistical approaches or from diferent empirical models. The data-driven approach combined with statistical regression analysis provides better understanding of relationship between variables and prediction of ROP