DSDE: In Theory

This paper presents the design and development of a prototype intelligent water-injection and smart allocation tool aimed at achieving autonomous waterflood operations.

This paper addresses the challenges of integrating huge amounts of data and developing model frameworks and systematic workflows to identify opportunities for production enhancement by choosing the best candidate wells.

Supervised learning was used to develop an ensemble of models that account for historical production data, geolocation parameters, and completion parameters to forecast production behavior of oil and gas wells.

The authors of this paper propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment.

This paper develops a deep-learning work flow that can predict the changes in carbon dioxide mineralization over time and space in saline aquifers, offering a more-efficient approach compared with traditional physics-based simulations.

This paper presents an approach using artificial neural networks to predict the discharge pressure of electrical submersible pumps.

This work provides a new modeling tool, validated against a static-wellbore solver and field data, to estimate and manage downhole temperature in higher-temperature oil, gas, and geothermal wells.

The authors of this paper propose an artificial-intelligence-assisted work flow that uses machine-learning techniques to identify sweet spots in carbonate reservoirs.

The authors of this paper investigate the application of two seismic monitoring methods in monitoring carbon leaks: full waveform inversion and reverse-time migration.

The main objective of this paper is to investigate the relationship between strain change and pressure change under various fractured reservoir conditions to better estimate conductive fractures and pressure profiles.