Quantifying the Effects of Interwell Communication Using Dynamic Fluid-in-Place Calculations
Bibliographic record
Abstract
Summary Multifractured horizontal wells (MFHWs) completed in the same reservoir layer, or different reservoir layers, commonly experience interwell communication through hydraulic fractures. For example, after a well is placed on production, its production performance can be impacted by communication with an offsetting well placed on production after it. The degree of communication between wells is important to quantify for the purposes of well production forecasting, reserves estimation, completions, and well spacing design optimization. In this study, dynamic fluid-in-place calculations, performed using the impacted producing well rates and flowing pressures, are applied to quantify the effect of communication with an offset-producing well on the impacted well-contacted fluid-in-place estimates. Agarwal (2010) demonstrated that pressure transient analysis theory can be used to derive the volume of fluid in place contacted by a well (CFIP) over time during constant rate, transient production. The method was later extended to variable-rate/pressure scenarios. However, all previous applications of Agarwal’s method were for single, isolated wells and assumed single-phase flow of oil and gas. To evaluate the usefulness of the method for modern development scenarios, it is extended to allow for quantification of interwell communication during flowback, for which single-phase flow of water before the breakthrough of formation fluids may precede multiphase flow of formation and fracturing fluids, and for analysis of multiphase data. Analysis of flowback data enables early-time identification and quantification of interference effects. Multiple numerical simulation cases are generated to simulate different degrees of communication for the case of a two-phase flow of oil and water. Wells are assumed to be communicating through a hydraulic fracture with a specified transmissibility multiplier (Tmult) used to adjust the amount of interwell communication. Corrections for multiphase flow in the CFIP method are performed using two different methods—the total volumetric flow rate (combined phase) approach and the multiphase pseudovariable approach. The CFIP diagnostic plot (i.e., log-log plot of CFIP vs. material balance time) is applied to the impacted producing well to evaluate the CFIP trend before and after offset well production and the magnitude of CFIP change. The practical application of the method is demonstrated with field cases. From the simulation cases, it is observed that, after the offset well is placed on production, a reduction of CFIP for the impacted producing well occurs (rapidly decreasing at first and then stabilizing after a transition period) proportional to productivity index reduction. The loss in CFIP for the impacted producing well can be determined simply by estimating its CFIP immediately before and after offset well production. For high connectivity (Tmult> 0.25) scenarios, application of the combined phase approach resulted in estimates of the impacted well CFIP reduction of ~46–50%, whereas application of the modified pseudovariable approach resulted in estimates of ~49–51%. For the low connectivity case (Tmult = 0.001), these estimates were ~11% and ~9%, respectively, for the two approaches. Therefore, for the simulation cases studied herein, the two approaches agreed within acceptable error. Numerical simulation was also used to verify the absolute change in CFIP using these two approaches for correcting for multiphase flow. The practical application of the modified CFIP method was demonstrated using two field cases with early-time production. Both field cases demonstrated that changes in CFIP for the impacted well can be unambiguously interpreted. In the first field case corresponding to early-time production data (gas and water) associated with Well 23 of the SPE data repository, the reduction in CFIP of the impacted producing well was estimated to be ~37% using the combined phase approach. In the second field case, for which a producing well completed in a low-permeability gas condensate reservoir is impacted by placing multiple offset wells on production at the same time, the reduction in CFIP of the impacted well was estimated to be ~20% using the combined phase approach. In this study, we demonstrate for the first time that CFIP calculations can be applied to quantify interwell communication between two wells during flowback or early-time production when multiphase flow occurs in the reservoir.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".