Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Graduate Theses, Dissertations, and Problem Reports

2020

Artificial Intelligence

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Top-Down Model Development Using Data Generated From A Complex Numerical Reservoir Simulation With Water Injection, Yvon Andrea Martinez Jan 2020

Top-Down Model Development Using Data Generated From A Complex Numerical Reservoir Simulation With Water Injection, Yvon Andrea Martinez

Graduate Theses, Dissertations, and Problem Reports

Numerical simulation and data-driven modeling are two current approaches in engineering reservoir modeling. Numerical reservoir simulation attempts to match past production history by modifying reservoir properties of the model. After multiple computationally intensive trial and error efforts, accurate history matches are identified. These history matches are used by project management for production forecasting purposes. Data-driven reservoir modeling utilizes measured data and is, therefore, free of assumptions that are often included in numerical reservoir simulations. Artificial intelligence and machine learning algorithms are technologies implemented in the development of a data-driven reservoir model with efforts to learn fluid flow through porous media …


Utilization Of A Numerical Reservoir Simulation With Water And Gas Injection For Verification Of Top Down Modeling, Ashley Konya Jan 2020

Utilization Of A Numerical Reservoir Simulation With Water And Gas Injection For Verification Of Top Down Modeling, Ashley Konya

Graduate Theses, Dissertations, and Problem Reports

The primary purpose of this thesis was to confirm the capabilities of artificial intelligence and machine learning through Top Down Modeling in history matching and predicting the oil, gas, and water production rates, reservoir pressure, and water saturation, of one limb of an anticline with water and gas injection. Several other characteristics were also applied to make the model more realistic to industry standards. The second purpose of this thesis was to determine the minimum amount of training and calibration data required in order to obtain good results for this particular dataset by increasing the blind validation in one year …