History Matching and Forecasting Tight Gas Condensate and Oil Wells by Use of an Approximate Semianalytical Model Derived From the Dynamic-Drainage-Area Concept
Bibliographic record
Abstract
Summary Recently, low-permeability (tight) gas condensate and oil reservoirs have been the focus of exploitation by operators in North America. Multifractured horizontal wells (MFHWs) producing from these reservoirs commonly exhibit long periods of transient flow, during which two-phase flow of oil and gas begins because of well flowing pressures dropping to less than saturation pressure. History matching and forecasting of such wells can be rigorously performed by use of numerical simulation, but this approach requires significant data and time to set up. Analytical methods, although requiring fewer data and less time to apply, have historically been developed only for single-phase-flow scenarios. In this work, a novel and rigorous analytical method is developed for history matching and forecasting MFHWs experiencing multiphase flow during the transient and boundary-dominated flow periods. The distance-of-investigation (DOI) concept has been used for many years in pressure-transient analysis to estimate distances of reservoir boundaries to wells, among other applications. In the current work, the DOI concept is used to estimate dynamic drainage area (DDA) to forecast tight gas condensate and oil wells; a linear flow geometry is assumed. During transient flow, the DDA is calculated at each timestep by use of the linear-flow DOI formulation; a multiphase version of the linear-flow productivity-index (PI) equation and material-balance equations for gas, condensate, and oil are solved iteratively for pressure, saturation, and fluid-production rate. The PI equations for gas and oil use pseudopressure, which is evaluated with saturation/pressure relationships derived from pressure/volume/temperature data. For boundary-dominated flow, when the drainage area is static, the inflow equations are again coupled with material balance for both phases. The new method is validated against numerical simulation, covering a wide range of fluid properties and operating conditions. The new method matches the simulation acceptably for all cases studied. Field examples of MFHWs are also analyzed to demonstrate the practical applicability of the approach. The three liquid-rich shale examples analyzed were also chosen to represent a wide range of fluid properties. In all cases, acceptable history matches are achieved. The new analytical forecasting/history-matching procedure developed in this work provides a practical alternative to numerical simulation for tight gas condensate and oil experiencing two-phase flow during the transient-flow period. The method, which does not rely on Laplace-space solutions, is conceptually simple to understand, easy to implement, and avoids the inconvenience of Laplace-space inversion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".