Digital Oilfields bringing new money to conventional production— Optimizing Business Models.

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In 2006, marketing commentator Michael Palmer had blogged “Data is just like crude. It’s valuable, but if unrefined it cannot really be used”.

Fast-forward thirteen years, the statement still holds true across any industry that depends on large volumes of data. It is true that until and unless, data is not broken down into pieces and analysed, it holds little value.

Data Science is a broad and sometimes confusing word, so it is likely that you have heard many different definitions of it. A general definition could be; “data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured.” While techniques and underlying principles of data science have been around for decades through various disciplines such as statistics, computer science, machine learning, and probability theory, it is only recently that data science as a unifying umbrella has received unprecedented attention and popularity, and rightfully so.

The recent technological improvements have resulted in daily generation of massive datasets in oil and gas exploration and production industries. It has been reported that managing these datasets is a major concern among oil and gas companies. A report by Brule stated that petroleum engineers and geoscientists spend over half of their time in searching and assembling data. Big Data refers to the new technologies in handling and processing these massive datasets. These datasets are recorded in different varieties and generated in large volume in various operations of upstream and downstream oil and gas industry. Moreover, in most cases, if processed efficiently, they can reveal important underlying governing equations behind sophisticated engineering problems. It is reported that based on the results of a survey conducted by General Electric and Accenture among the executives, 81% of them considered Big Data to be among the top three priorities of oil and gas companies. Based on their paper, the main reason behind this popularity is the need for improving the oil and gas exploration and production efficiency.

Traditionally, the oil and gas industry has been collecting a variety of data, such as production data, log data, geological data, completion data, artificial lift data, maintenance records, and data from permanent downhole sensors. However, often, most of the datasets generated in the industry are not fully utilized for efficient optimization, and almost certainly not enough so for proactive decision making. For example, water breakthrough in waterfloods can be a significant problem, resulting in reduced sweep efficiency and ultimate recovery. But, due to reservoir heterogeneity, it is generally hard to predict. Thus, operators mostly resort to reactive control, i.e., trying to remediate the well after it has broken through. These reactive solutions are usually “too little, too late,” and can be quite costly.

Come the PermianChain which comprises of a team that is certainly aware of this problem and has, over the last year or so, emphasized “digital oil fields” or “smart fields,” which are implementations of the concept of “closed-loop reservoir management.” This concept aims to continuously maximize the life-cycle value of oil and gas assets by real-time monitoring, continuous updating of predictive models with latest data, and continuous optimization of multiple long- and short-term decisions.

With our team’s initial focus targeted at bringing newfound liquidity to the sector through real-time capital injection as and when proven reserves are identified, with Data Science implementation to optimize marginal fields, ultimately allows increase adoption of the “digital oilfields” on a permissioned trust-protocol that timestamps oil production processes and tracks sources of oil trading activities.

Our next article will elaborate on how the team plans to roll out this newfound business model enabler.