Incorporation of Formation Water into Rate-Transient Analysis of Tight Oil Wells with High Water-Oil Ratio: A Field Example from North America
Bibliographic record
Abstract
Abstract Analysis of water production from multi-fractured horizontal wells in tight oil reservoirs has been mostly limited to analytical and/or numerical modeling of early-time fracture water production during flowback. During the flowback period, flush production of water occurs with a rapid decline in water rate. The rapid decline in water rate has tempted practitioners to ignore the impact of water production on rate-transient analysis (RTA) of multi-fractured horizontal wells (MFHWs) during the online production period. However, some tight oil reservoirs exhibit high water-oil ratios (WORs) throughout their production history. In these cases, water production should be included in RTA to ensure accurate hydraulic fracture characterization, short- and long-term rate forecasting, and fluid-in-place estimation, amongst other applications. In this study, two analytical methods are developed for incorporating formation water production into RTA of tight oil reservoirs producing during the transient linear flow period based on two scenarios: 1) water and oil producing from the same reservoir layer and 2) water and oil producing from different reservoir layers. For the first scenario, an estimate of formation water saturation is obtained using WOR history which, in turn, is used in an analytical method (facilitated by decoupling of phase saturations and pressure) to correct the single-phase oil or two-phase oil and gas RTA results. For the second scenario, the linear flow parameters calculated from corrected linear flow analysis of hydrocarbon (single-phase oil or two-phase oil and gas) and water layers are combined to obtain the total linear flow parameter. Practical application of the proposed analytical methods is demonstrated using a field example from a North American tight oil reservoir (with formation WOR ~ 1.3). The studied MFHW was first history-matched using the two water production scenarios – the linear flow parameter (Aki) was then calculated based on the simulation model match parameters. The results of the analytical methods used to correct for water production (for the two scenarios) were then compared with the numerical simulation results and determined to be within acceptable engineering error (10 %). This study further reveals that neglecting formation water production in RTA of this well leads to unacceptable errors (exceeding 100%) in the linear flow parameter estimates. The current study provides novel practical methods for correcting RTA to account for high formation water production. The methods are proved to be robust, easy to implement, and effective in reducing the errors of the widely used single-phase oil, and recently developed two-phase oil and gas, RTA models.
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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".