Status of Data-Driven Methods and their Applications in Oil and Gas Industry

Balaji, Karthik (University of North Dakota) | Rabiei, Minou (University of North Dakota) | Canbaz, Hakan (Schlumberger) | Agharzeyva, Zinyat (Texas A & M University) | Tek, Suleyman (University of the Incarnate Word) | Bulut, Ummugul (Texas A&M University-San Antonio) | Temizel, Cenk (Aera Energy LLC)



Data-driven methods serve as a robust tool to turn data into knowledge. Historical data generally has not been used in an effective way in analyzing processes due to lack of a well-organized data, where there is a huge potential of turning terabytes of data into knowledge. With the advances and implementation of data-driven methods data-driven models have become more widely-used in analysis, predictive modeling, control and optimization of several processes. Yet, the industry overall is still skeptical on the use of datadriven methods, since they are data-based solution rather than traditional physics-based solutions; even though physics and geology are sometimes part of this methodology. This study comprehensively evaluates the status of data-driven methods in oil and gas industry along with the recent advances and applications.

This study outlines the development of these methods from the fundamentals, theory and applications of these methods along with their historical acceptance and use in the industry. Major challenges in the process of implementation of these methods are reviewed for different areas of applications. The major advantages and drawbacks of data-driven methods over the traditional methods are discussed. Limitations and areas of opportunities are outlined. Latest advances along with latest applications and the associated results and value of the methods are provided.

It is observed that the successful use of data-driven methods requires strong understanding of petroleum engineering processes and the physics-based conventional methods together with a good grasp of traditional statistics, data mining, artificial intelligence and machine learning. Data-driven methods start with a data-based approach to identify the issues and their solutions. Even though data-driven methods provide great solutions on some challenging and complex processes, that are new and/or hard to define with existing conventional methods, there is still skepticism in the industry on the use of these methods. This is strongly tied to the delicacy and sensitive nature of the processes and on the usage of the data. Organization and refinement of the data turn out to be important components of an efficient data-driven process.

Data-driven methods offer great advantages in the industry over that of conventional methods under certain conditions. However, the image of these methods for most of the industry professionals is still fuzzy. This study serves to bridge the gap between successful implementation and more widely use and acceptance of data-driven methods, and the fuzziness and reservations on the understanding of these methods in the industry. Significant components of these methods along with clarification of definitions, theory, application and concerns are also outlined in this study.