Across oil exploration, production, transportation, and refinement, the oil and gas sector has long been at the cutting edge of technology, pioneering incredible feats of engineering. In recent years, the oil and gas industry has been catching up to other sectors in the machine learning space, thanks in part to better data handling and processing.
Low commodity prices and negative investor sentiment have also pushed operators to modernize operations.
Machine learning enables the industry to learn from the vast amounts of data generated during oil and gas operations by improving operational efficiency and decision-making. Machine learning helps oil and gas businesses save time, reduce risks, and improve return.
But how exactly does machine learning benefit the oil and gas companies? Of course, it varies depending on the workflow.
At the far upstream end, machine learning can speed up well log or seismic interpretation. In this area, geoscientists work with reinforcement learning algorithms or supervised learning algorithms, providing stratigraphic picks that are then propagated around a dataset to generate widespread interpretations, quickly. This can save a geologist hundreds of hours of work. Less time, fewer errors, and consistent outcomes translate into lower expenses. Unsupervised algorithms can also be used to classify log or seismic facies, potentially helping identify previously unrecognized rock groups.
Within the unconventional oil and gas space, many basins now have tens of thousands of producing wells, a treasure trove of data to analyze with statistical algorithms. Across these datasets, operators have tried many different combinations of completions designs and well spacing configurations, implemented across many different geologic settings. Supervised machine learning models can use these data types (completions, geology, spacing) as the training variables, with the “labels” being production data (what the model is trying to predict).
From all this data, the models learn how the input variables impact the well production. After the model is trained, it can be used to forecast hypothetical designs, such as forecasting production at proppant intensity of 2000, 2250, 2500, 2750, and 3000 lbs/ft. Combining that with a cost model and price deck will give you a data-driven optimization on proppant intensity. This type of use case is core at Novi Labs, where our machine learning platform generates these forecasts using mostly decision-tree based methods. Because humans struggle to analyze the data across many dimensions, the machine learning models can provide a more accurate answer, potentially unlocking hundreds of thousands or millions of dollars of value at each well location by improving production or saving costs.
Other field development use cases include identifying the optimum number of wells, locations, or sequence of drilling. Machine learning can help identify geologic sweet spots and determine the profitability of each zone at a given location. These models also improve baseline forecast accuracy over type curve methods, because they minimize bias and effectively analyze well performance across many dimensions.
Machine learning in oil and gas is also used for production engineering and midstream applications. One promising technology in the space is virtual flow metering, which estimates flow rates based on pressure, temperature, and choke data.
Midstream companies can use machine learning for leak detection, preventative maintenance, and transportation optimization.