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Dynamic Shale Analytics

Managing & Mitigating Frac-Hit; AI-Based Shale Reservoir Management.

Realistic, AI-Based Reservoir Simulation for Shale

Dynamic Shale Analytics is a new technology that combines pattern recognition capabilities of Artificial Intelligence and Machine Learning with the domain knowledge related to reservoir, completion, and production engineering in order to address, manage, and mitigate Frac-Hit. Our decades of research and development in reservoir engineering and reservoir modeling that has been augmented by the state of the art in Artificial Intelligence and Machine Learning has recently resulted in a major conclusion. We have come to the conclusion that using well productivity snapshots in time as output of the models or analyses cannot provide the type of tools that are required for addressing issues such as Frac-Hit.

We have concluded that Frac-Hit prediction, management, and mitigation require modeling the dynamics of each individual well in the context of reservoir dynamics, also known as reservoir simulation and modeling. However, the problem is that such models cannot be developed using traditional numerical reservoir simulation and modeling technology that is currently being used in our industry. The solution is "Dynamic Shale Analytics", a comprehensive, fully data-driven, AI-based reservoir simulation and modeling.

In "Dynamic Shale Analytics" daily oil, gas, and water production, as well as reservoir pressure are history matched for every individual well using Choke Setting and/or wellhead pressure (operational conditions) as input to the model. Dynamic Shale Analytics introduces a new technology for building a comprehensive, full field, dynamic and coupled reservoir-wellbore simulation model that avoids any and all assumptions and simplifications. Dynamic Shale Analytics is a reservoir+wellbore simulation model based only on field measurements and data that is gathered from drilling, completion, operation, and production of all the shale wells in a given asset. Dynamic Shale Analytics has the capability of simultaneously history matching hundreds or thousands of shale wells that have been drilled through hundreds of pads.

Dynamic Shale Analytics discovers the patterns of induced fractures at every cluster as a function of well placements, reservoir characteristics, and completion practices, as well as operational constraints in order to predict and mitigate interactions between multiple Parent and Child wells. Completion optimization, well spacing and stacking, as well as management of operational constraints (choke setting and wellhead pressure) for the production optimization are part of the Dynamic Shale Analytics deliverable.

Other operations such as re-frac candidate selection and design as well as recovery optimization and production enhancement by gas injection into the shale wells can also be modeled realistically through Dynamic Shale Analytics using the pattern recognition capabilities of Artificial Intelligence and Machine Learning. Current limitations associated with the application of Artificial Intelligence and Machine Learning regarding the completion and production optimization is mainly due to the static nature of the modeling and data analytics techniques that are used for this purpose. In order to model Frac-Hit, dynamic interaction between multiple parent and child wells must be modeled as a function of time throughout the life of each shale well. Dynamic Shale Analytics overcomes these limitations and accommodates comprehensive dynamic reservoir and field management in a unique and unmatched manner.