Modelling the Miscible Displacement in CO-ECBM Using the Convection-Dispersion with Adsorption Model
Bibliographic record
Abstract
Abstract The convection-dispersion model with adsorption has been used successfully to evaluate adsorption of foam-forming surfactants from core flooding experiments in enhanced oil recovery applications. Mechanisms that determine the transport of CO2 gas through coal in CO2-ECBM include convection, dispersion and adsorption. This work attempts to model the CO2-ECBM process utilizing the convection-dispersion model with adsorption. A laboratory experiment was conducted to simulate the CH4 miscible displacement by CO2 gas using a coal core sample from Alberta Mannville Formation. The effluent gas compositions were monitored by gas chromatography. The transport equation for dispersion and adsorption of CO2 in coal, considering Langmuir equilibrium adsorption was solved numerically. The results show that the transport of CO2 in coal resulting from dispersion and adsorption can be modeled successfully. The effects of various properties and process parameters such as porosity, Peclet number, injection gas pressure, and Langmuir parameters on gas adsorption and CO2 breakthrough were also investigated and discussed. Introduction Adsorption during flow through porous media is of interest in a number of disciplines, such as adsorptive separation processes, chromatography, soil science, and improved oil recovery. A large number of references on the subject exist(1). Miscible displacement and dispersion coupled with adsorption phenomena therefore occur in many important fields of technology, including petroleum reservoir engineering. Mechanisms that determine the transport of the displacing fluid through a porous medium include convection, dispersion and adsorption. The convection-dispersion model with adsorption has been used successfully to evaluate adsorption of a number of foam-forming surfactants from core flooding experiments in enhanced oil recovery applications(2, 3, 4). The adsorption of polymer and surfactant solutions on porous rocks is complicated by the physiochemical properties of the solutions and rocks andby the nature of the pore structure of the rock matrix. In CBM industry, reservoir modeling and simulation is essential to predict the gas recovery. The transport of CO2 gas through coalbed in CO2-ECBM process is a typical miscible displacement. In commercial CBM simulators, this process is commonly modeled by multi-component adsorption/diffusion in coal matrix with multi-phase Darcy flow in cleat system (i.e. GEM by Computer Modeling Group). This work attempts to utilize the convection-dispersion model with adsorption to model the behavior of CO2 gas flow in CO2-ECBM process and at the same time to investigate the effects of the various properties and process parameters numerically. CO2-ECBM Experiment Sample Preparation The laboratory CO2-ECBM process has been conducted by a core-flood experiment. The coal sample is a Deal Bruce core plug mined from the Manville formation in Alberta. Table 1 lists the major important characteristics of the coal sample. Prior to starting the experiments; the coal core sample was kept in a moisture chamber (25 °C, 30 mmHg) over saturated K2SO4, to simulate reservoir conditions. Proximate analysis was carried out on crushed coal sub-samples to determine moisture (ASTM D3173), ash (ASTM D3174), and volatile matter (ASTM D3175) contents(5). According to the coal rank characterization, the rank of this piece of coal is sub-bituminous B indicated byits 0.47% vitrinite reflectan
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".