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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

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Related Experiment Video

Updated: Jul 10, 2026

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

Predicting CO2 injection profiles in heterogeneous reservoirs using a physics-aware deep learning framework.

Zihao Zheng1, Haoxi Shi2, Xintong Liu1

  • 1School of Future Technology, Yangtze University, Jingzhou, 434023, China.

Scientific Reports
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for predicting carbon dioxide (CO2) injection profiles in reservoirs. The framework accurately forecasts layer-wise CO2 injection, optimizing strategies for carbon capture, utilization, and storage (CCUS) and enhanced oil recovery (EOR).

Keywords:
Bi-LSTMCO2 injection profileCO2-EORFeature-wise Linear Modulation (FiLM)Heterogeneous reservoirsSelf-attention mechanism

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Area of Science:

  • Reservoir Engineering
  • Machine Learning
  • Computational Science

Background:

  • Accurate prediction of CO2 injection profiles is crucial for optimizing injection strategies in heterogeneous reservoirs.
  • Traditional reservoir simulations are computationally expensive for multi-scenario analysis and optimization.
  • Existing machine learning models struggle to capture both temporal dynamics and geological heterogeneity.

Purpose of the Study:

  • To develop a deep learning surrogate model for one-step-ahead prediction of layer-wise CO2 injection profiles.
  • To accelerate the evaluation of multiple geological and operational scenarios for CO2 injection.
  • To support optimized layer-wise injection allocation in CO2-Enhanced Oil Recovery (EOR) and Carbon Capture, Utilization, and Storage (CCUS) applications.

Main Methods:

  • Generated a large-scale dataset using the ECLIPSE compositional simulator.
  • Developed a deep learning framework integrating Bidirectional Long Short-Term Memory (Bi-LSTM), self-attention, and Feature-wise Linear Modulation (FiLM).
  • Bi-LSTM captures temporal dependencies, attention weights influential time steps, and FiLM incorporates geological heterogeneity.

Main Results:

  • The proposed model achieved high accuracy, outperforming LSTM baselines with a Mean Absolute Error (MAE) of 14.28 ± 0.41 m³/d and R² of 0.9915 ± 0.0024.
  • Ablation studies confirmed the significant contribution of each module (Bi-LSTM, attention, FiLM) to predictive performance.
  • The model demonstrated stable performance across various injection scenarios, including continuous gas injection, Water-Alternating-Gas (WAG), and shut-in operations.

Conclusions:

  • The deep learning framework provides an efficient, data-driven surrogate for CO2 injection profile prediction.
  • This approach accelerates multi-scenario analysis and aids in optimizing injection strategies for CCUS and CO2-EOR.
  • The model effectively integrates temporal dynamics and static geological features for improved prediction accuracy.