The complexity of physics-based modeling of fluid flow in hydraulically fractured unconventional reservoirs, together with the abundant data from repeated factory-style …
To improve the accuracy of short-term multiphase production forecasts with one-step-ahead predictions, a Contextual Bi-directional Long Short-Term Memory (C–Bi-LSTM) was …
Data-driven methods have surged in popularity due to increased field development and data collection effort in the last two decades, and partly because flow physics in hydraulically …
Carbon sequestration is a promising technique to minimize the emission of CO2 to the atmosphere. However, the computational time required for CO2 forecasting using …
H Cheng, Y He, P Zeng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Subsurface flow is the core of reservoir engineering. Research on subsurface flow problems can enhance our understanding of the development status of oilfields, thus enabling the …
Given sufficiently extensive data, deep-learning models can effectively predict the behavior of unconventional reservoirs. However, current approaches in building the models do not …
Constructing reliable data-driven models to predict well production performance (eg, estimated ultimate recovery, cumulative production, production curves, etc.) for …
Geological carbon storage (GCS) is the most popular technique for sequestering CO2. Usually, GCS is modeled using commercial numerical simulators to make CO2 forecasts for …
Developing a reliable deep learning model for new unconventional reservoirs, is often constrained by the limited number of wells available. Transfer learning is a useful approach …