SHALE Website Featured Nov Dec 2018 Data Analytics

Unlocking performance through data analytics and Integration

The turbulent oil and gas landscape over the last few years has forced the oil and gas industry to a deep introspection and re-examination of the changes needed to adapt to an environment of unpredictable oil price. This situation has lent real urgency to the efforts to improve operational efficiency and invest in technology and innovation.

Across the whole unconventional space, operators are investing in established and conventional technologies at their disposal and are successful in capturing some value from those investments. In the Eagle Ford, for instance, operators have been able to reduce drilling and completion costs through: multi-well pad design; optimizing completion designs by modeling well interference and using descriptive tools for infill drilling; proppant volumes, water chemistry and well spacing have been adjusted accordingly to maximize the return on capital deployed; refracturing the parent well independently or before completing the offset child wells to boost production in both the parent and child wells. Also, enhanced oil recovery (EOR) applications using natural gas injection is delivering positive production results and is emerging as a potential opportunity to further increase production in both the parent and child wells.

Despite the glaring advances, the hard truth is that most operators are lagging behind and are not maximizing the value potential of their assets. Many well-intentioned engineers and scientists who are involved in the day-to-day operations of shale assets develop a simplistic and deterministic view that enables them to make the best of what is available to them in order to increase the efficiency and the recovery from the shale wells. However, the transition to infill development in the shale fields has brought additional technical complexities, and production rates have been highly variable and unpredictable. This is due to a multitude of factors including depletion effects of the parent well that can cause fracture hits and inter-well communication among child and parent wells. Significant depletion caused by parent wells is complicating the performance prediction of new infill wells. Geological heterogeneity is still poorly understood. Although operators expect infill wells to perform comparably to, or better than, existing parent wells, the reality is that infill wells often produce below the established offset parent well decline curves. This scenario adversely impacts future reserve estimates and, ultimately, field economics.

There are many opinions and speculations on what exactly happens and what to do as we embark upon drilling, completing and hydraulically fracturing shale wells. It is however self-evident that effectively capturing the value of a shale asset require going beyond simple intuitions and develop a solution more adequate for a multi-dimensional problem. The fact remains that, as an industry, we collect a significant amount of data during the operations that result in oil and gas production. It is not unrealistic to assume that the collected data contains the knowledge we need in order to optimize production and maximize recovery from this prolific hydrocarbon resource.

To mitigate operational inefficiencies, and raise the overall shale assets performance, operators must embrace advanced petroleum data analytics and integration. The rapid advances in computer science have enabled the development of readily-available powerful tools that use a combination of state-of-the-art engineering, data science, and computing power to identify superior solutions to complex production optimization problems. Using these tools to supplement conventional models will improve the physical understanding of shale assets and alleviate the gaps in performance that hold back production. A few operators are moving in this direction and are already capturing significant benefits using simple predictive analytics to catch failures before they happen. This technology is transforming maintenance strategies from reactive to proactive, effectively reducing unplanned downtime and improving asset utilization.

It should be stressed that realizing the full value of data analytics require that we alter how we do things. Data analytics and integration mean new ways of thinking and working, new collaborations structure and a boundaryless organization. Data analytics and integration across different functions (reservoir, completion, drilling, geology, finance) will enable the development of a unified view of the asset and create a system of continuous learning from the data generated on a regular basis. Insights from such integration will improve reservoir models to better account for the real impact of infill wells, to fully understand critical timing, spacing and job sizes, to solve these dynamic challenges related to field development planning in unconventional basins.

Adopting data analytics and integration brings improvements but might also bring new challenges — however, these are solvable. It is crucial to revisit and upgrade accepted organizational decision-making processes and risk-management techniques. It is also important to develop new standards, procedures and methodologies and to apply historical experiences only in new ways.

Data analytics is an is an exciting opportunity that enables not only that each molecule of hydrocarbon produced brings a return to our investment, but also enriches our understanding of how this resource should be managed for sustainable return. We are just at the start of the development curve for much leaner, efficient industry and this technology is unquestionably a big part of the solution.

About the Author: Denis Pone is an oil and gas entrepreneur and inventor. He is currently the President and CEO of Olympia Energy — a private oil and gas company with the main strategy focused on creating value through optimization & rejuvenation of its operated oil and gas assets. Pone has an MBA from the University of Massachusetts at Amherst and began his career as a reservoir engineer with ConocoPhillips after his doctorate from Penn State.

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