The Energy industry needs to constantly adapt to the quickly changing business landscape. The companies are betting on digital transformation as a way to innovate and grow. This webinar event will feature two technical talks demonstrating data analytics as a key ingredient in delivering improved productivity and higher efficiency. The talks will be followed by a virtual networking opportunity.
Abstract Case Study #1:
Several years ago the analytic workflow Type-Curve Optimizing Geostatistical Array (TOGA) was introduced in the industry to tie unconventional reservoir and well completion characteristics to historical production and make robust predictions of well performance. TOGA is built on a foundational geologic framework and leveraging random forest machine learning optimization for subsurface data sets. Random forest methods are preferred for unconventional systems because they capture complex, multidimensional, and non-linear interactions in noisy databases. TOGA is part of a portfolio of other machine learning and physically-based reservoir engineering tools used by companies to reduce uncertainty in unconventional reservoir performance prediction. The workflow generates performance prediction maps by applying the random forest multivariate relationships to grids of the key reservoir predictor variables. These production prediction maps are stacked to form an array of locations that each have a unique expected production profile through time. TOGA output may be used to calibrate existing type curve workflows, define reservoir sweet spots, establish reservoir continuity, and predict ultimate recovery. Data science and machine learning approaches have progressed the organization’s understanding of its assets, especially in the Permian Basin. Unconventional plays were once thought to be relatively unpredictable, highly variable, and have little connection to reservoir properties. TOGA and other proprietary data analytic technologies have enabled the identification of key reservoir performance drivers for each unconventional target zone under development.
Abstract Case Study #2:
In this talk, we will showcase a pragmatic, value-focused, agile approach to the digital transformation of upstream oil and gas production operations. We will discuss how this approach enables oil and gas companies to deliver a set of ‘mutually reinforcing’ data analytics solutions to transform their end-to-end workflows and unlock significant value from their producing assets. To illustrate the impact of digital solutions on production operations, we will highlight specific use cases that have delivered game-changing results in the production surveillance and optimization space. Finally, we will discuss some common pitfalls in AI-driven transformation in upstream oil and gas, and some recommendations for how companies can avoid these pitfalls.