Numerical simulation of heat extraction performance in enhanced geothermal system with multilateral wells

This paper reviews basic results and recent developments in the field of small-scale gaseous hydrodynamics which has received significant attention in connection with small-scale science and technology. We focus on the modeling challenges arising from the breakdown of the Navier-Stokes description, observed when characteristic lengthscales become of the order of, or smaller than, the molecular mean free path. We discuss both theoretical results and numerical methods development. Examples of the former include the limit of applicability of the Navier-Stokes constitutive laws, the concept of second-order slip and the appropriate form of such a model, and how to reconcile experimental measurements of slipping flows with theory. We also review a number of recently developed theoretical descriptions of canonical nanoscale flows of engineering interest. On the simulation front, we review recent progress in characterizing the accuracy of the prevalent Boltzmann simulation method known as direct simulation Monte Carlo. We also present recent variance reduction ideas which address the prohibitive cost associated with the statistical sampling of macroscopic properties in low-speed flows.

http://absimage.aps.org/image/DFD05/MWS_DFD05-2005-020061.pdf

The limits of Navier-Stokes theory and kinetic extensions for describing small-scale gaseous hydrodynamics

Alkaline–surfactant–polymer (ASP) flooding is a combination process in which alkali, surfactant, and polymer are injected in the same slug. Because of the synergy of these three components, ASP is the current worldwide focus of research and field trial in chemical enhanced oil recovery (EOR).

This paper is to provide a comprehensive review of the ASP process. The reviewed topics include the following: 

ASP synergy and its EOR mechanisms
Screening criteria
Laboratory work
Numerical simulation work
Summary of pilots and large-scale applications
Project economics
Chemicals used
Water quality
Mobility control requirement
Problems associated with ASP flooding and possible solutions


In addition to the comprehensive review, future developments are also discussed. Data and analyzed results presented in this review will provide the reader with the updated information about ASP and a guide to read relevant papers for those who are new to chemical EOR. The survey data also provide operators with some reference data for their project design and optimization. © 2014 Curtin University of Technology and John Wiley & Sons, Ltd.

A comprehensive review of alkaline–surfactant–polymer (ASP) flooding

The adsorption behavior and underlying mechanism of CO2 and CH4 binary mixture in shale kerogen significantly affect the CO2 sequestration with enhanced gas recovery project (CS-EGR). In this study, we investigated the competitive adsorption behaviors of CO2 and CH4 in shale kerogen nanopores using grand canonical Monte Carlo (GCMC) method. Kerogen model takes into effect of matrix and slit nanopores and moisture content based on Ungerer’s molecular model and scanning electron microscope (SEM) analysis, and is successfully validated against experimental data. The effects of temperature, CO2 and CH4 distribution, moisture content, adsorption selectivity, and optimal formation for injection were discussed. The results show that adsorption amount of CH4 on the kerogen increases with increasing pressure and decreases with increasing temperature. The adsorption selectivity of CO2 over CH4 is 2.53–7.25, which indicates that CO2 is preferentially adsorbed over CH4 under different temperatures. H2O prefers to ads...

Molecular Simulation of CO2/CH4 Competitive Adsorption on Shale Kerogen for CO2 Sequestration and Enhanced Gas Recovery

A Comprehensive Review of Alkaline-Surfactant-Polymer (ASP) Flooding

Fracture Initiation and Morphology of Tight Sandstone by Liquid Nitrogen Fracturing

A Semianalytical Method for Modeling Two-phase Flow in Coalbed Methane Reservoirs with Complex Fracture Networks


 Coalbed methane (CBM) has emerged as one of the clean unconventional resources to supplement the rising demand of oil and gas. Analyzing and predicting CBM production performance are critical in choosing the optimal completion methods and parameters. However, the conventional numerical simulation has challenges of complicated gridding issues and expensive computational costs. The huge amount of available production data that has been collected in the field site opens up a new opportunity to develop data-driven approaches in predicting the production rate. Here, we proposed a novel physics-constrained data-driven workflow to effectively forecast the CBM productivity based on a gated recurrent unit (GRU) and multilayer perceptron (MLP) combined neural network (GRU-MLP model). The model architecture is optimized automatically by the multiobjective algorithm: nondominated sorting genetic algorithm Ⅱ (NSGA Ⅱ). The proposed framework was used to predict gas and water production in synthetic cases with various fracture-network-complexity/connectivity and two multistage fractured horizontal wells in field sites located at Ordos Basin and Qinshui Basin, China. The results indicated that the proposed GRU-MLP combined neural network was able to accurately and stably predict the production performance of CBM fractured wells in a fast manner. Compared with recurrent neural network (RNN), GRU, and long short-term memory (LSTM), the proposed GRU-MLP had the highest accuracy, stability, and generalization, especially in the peak or trough and late-time production periods, because it could capture the production-variation trends precisely under the static and dynamic physical constraints. Consequently, a physics-constrained data-driven approach performed better than a pure data-driven method. Moreover, the contributions of constraints affecting the model prediction performance were clarified, which could provide insights for the practicing engineers to choose which categorical constraints are needed to focus on and preferentially treated if there are uncertainties and unknowns in a realistic reservoir. In addition, the optimum GRU-MLP model architecture was a group of optimized solutions, rather than a single solution. Engineers can evaluate the tradeoffs within this optimal set according to the field-site requirements. This study provides a novel machine learning approach based on a GRU-MLP combined neural network to estimate production performances in naturally fractured reservoir. The method is gridless and simple, but is capable of predicting the productivity in a computational cost-effective way. The key findings of this work are expected to provide a theoretical guidance for the intelligent development in oil and gas industry.

A Physics-Constrained Data-Driven Workflow for Predicting Coalbed Methane Well Production Using Artificial Neural Network

Slotted liner sheathing coiled tubing-a new concept for multilateral jetting in coalbed methane wells and laboratory tests of tubular friction performance

Ruiyue Yang

Papers

Fracture Initiation and Morphology of Tight Sandstone by Liquid Nitrogen Fracturing

A Semianalytical Method for Modeling Two-phase Flow in Coalbed Methane Reservoirs with Complex Fracture Networks

A Physics-Constrained Data-Driven Workflow for Predicting Coalbed Methane Well Production Using Artificial Neural Network

Slotted liner sheathing coiled tubing-a new concept for multilateral jetting in coalbed methane wells and laboratory tests of tubular friction performance

Comparison of Capillary Pressure-Saturation Models for Gas-Water Systems in Shale Gas Reservoirs