ISI | AEM ENERSOL

Our Partners

AEM Enersol partners with Intelligent Solutions, Inc. (ISI) to provide services in data driven reservoirs models (Top-Down, Intelligent Reservoir Modelling) at a fraction of the time and budget of numerical simulation models.

Intelligent Solutions, Inc. (ISI) is the leading the upstream oil and gas industry in Artificial Intelligence (AI) & Data Mining (DM) based consulting services, workflows and software. ISI’s strength stems from the fact that it has been founded by oil and gas domain experts with decades of practical experience in the art and science of machine learning and data analytics. Rather than providing tools so that others can use to build workflows and solutions, ISI has concentrated on providing specific solutions to address specific needs in the industry.

Product & Services

Top Down Modelling

Top-Down Modeling (TDM) is a realistic, tested, and validated alternative to traditional Numerical Reservoir Simulation and Modeling. TDM is a technology that combines reservoir engineering, reservoir modeling, and reservoir management with state-of-the-art Artificial Intelligence and Machine Learning in order to build a thorough and comprehensive, full field, coupled, reservoir-wellbore model.

During the development of the Top-Down Model for a given mature field all the wells in the field are history matched. The history matching is performed simultaneously for all dynamic variable such as Oil rate, Gas, and Water production, as well as Reservoir Pressure, and Water Saturation. Following is an example of such a history match for one of the wells in a mature field in the Middle East.

Smart Proxy Modelling

Smart Proxy Model is a realistic, tested, and validated alternative to traditional proxy models known as Reduced Order Models (ROM) or statistical Response Surfaces. Samrt Proxy is a technology that combines reservoir engineering, reservoir modeling, and reservoir management expertise with state-of-the-art Artificial Intelligence and Machine Learning in order to build highly accurate and very fast proxy model for highly complex numerical reservoir simulation models.

Shale Analytics

ISI’s unique, proprietary, and innovative Shale Analytics technology has been applied to more than 7000 wells in Marcellus, Utica, Niobrara, Codell, Eagle Ford, Bakken, and Permian. Shale Analytics uses only “Hard Data” and is based on the augmentation of reservoir, completion, and production engineering with the state-of-the-art Artificial Intelligence and Machine Learning. Shale Analytics technology includes three modules: Descriptive Analytics, Predictive Analytics & Prescriptive Analytics.

Descriptive Analysis

Predictive Analysis

Prescriptive Analysis

Dynamic Shale Analytics

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.

Publications

Top-Down Modelling: A Shift in Building Full-Field Models for Mature Fields: Young Technology Showcase – JPT July 2016
Full Field Reservoir Modelling of Shale Assets using Advanced Data-Driven Analytics: “Geoscience Frontiers”, 2015.
Top-Down, Intelligent Reservoir Modelling of Oil and Gas Producing Shale Reservoirs: Case Studies. “International Journal of Oil, Gas and Coal Technology”. Vol. 5, No.1, 2012 pp. 3 – 28 – DOI: 10.1504/IJOGCT.2012.044175
Intelligent Production Modelling Using Full Field Pattern Recognition. “SPE Reservoir Evaluation and Engineering Journal”. Volume 14, No. 6, December 2011, pp. 735-749. DOI: dx.doi.org/10.2118/132643-PA.
Reservoir Simulation and Modelling Based On Artificial Intelligence and Data Mining (AI&DM). “Journal of Natural Gas Science and Engineering”. Volume 3 (2011), pp. 697-705.