A New Macroscopic Nucleation Model for Simulation of the Solution Gas Drive in Heavy Oils
Bibliographic record
Abstract
Abstract Presence and behaviour of solution gas-drive effect appears to be critical to the cold production process. This process is not a well-understood production mechanism because a wide range of different petrophysical parameters and experimental factors interact in a rather complex way. Over the past years, number of efforts has been made, in many institutions, in order to understand and model the solution gas-drive mechanism in primary heavy oil recovery. Conventional simulations succeed in matching actual field productions but are not reliable for prediction forecast purposes (large uncertainties on recovery factors). If matching work on long core depletion experiments is satisfactory in terms of oil and gas productions, it fails to predict the gas saturation gradients. In this context, it has been stated that it was necessary to develop new tools in order to:Test efficiently new modelling approaches, allowing sensitivities to physical measurable parametersSimulate and match heavy oil long core depletion experiments data (productions, gas saturation gradients)Issue recommendations to improve current simulation tools. In this paper, we present a new 'macroscopic' approach, at the Darcy's scale whose advantages are that of a modelling by a continuous equations system, with a limited number of parameters, having a physical meaning (measurable). The development of this phenomenological code is ongoing in order to account for the fundamental steps of the depressurisation process, from nucleation of bubbles, to their growth by solute diffusion and expansion, to the final stages of coalescence, migration, and production. Introduction Over the past several years, a number of efforts have been made, in many institutions, to understand and to model thesolution gas drive mechanism in primary heavy oil recovery. Even though conventional simulations can succeed in matching field productions, they fail to capture the actual physics of the solution gas drive process in heavy and extra-heavy oils, thus leading to unreliable forecasts. Therefore, an extensive research on the evaluation of the solution gas drive process during cold production of extra heavy oils has been launched to derive a pragmatic and predictive representation of the phenomena at a macroscopic scale. This work is intended to assess the existence of the "foamy oil"up (1) - or more accurately 'bubbly' oil - effect and to quantify its relative importance at field scale. Figure 1 presents some results from various laboratory depletion experiments (conducted by several institutes or companies, including Total) and from some modeling attempts with commercial simulators (Stars ®, Eclipse ®), where recoveries are plotted as a function of depletion rates. The reservoir simulations, extrapolated over 35 years, are based on relative permeabilities and kinetics parameters that match laboratory experiments at two different reservoir depletion rates (2, 8). As shown on the plot, although the parameters of the simulations are chosen to reproduce laboratory experiments, the field scale simulations highlight significantly different final recoveries, depending on the simulator and/or model selected. Given the huge oil in place in Canadian and Venezuelan oil fields, such a range of possible ultimate recovery factors is not acceptable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".