Diagnosis of Reservoir Behavior From Measured Pressure/Rate Data
Bibliographic record
Abstract
Diagnosis of Reservoir Behavior From Measured Pressure/Rate Data C. Shah Kabir; C. Shah Kabir Chevron Corp. Search for other works by this author on: This Site Google Scholar Bulent Izgec Bulent Izgec Texas A&M University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, May 2006. Paper Number: SPE-100384-MS https://doi.org/10.2118/100384-MS Published: May 15 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Kabir, C. Shah, and Bulent Izgec. "Diagnosis of Reservoir Behavior From Measured Pressure/Rate Data." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, May 2006. doi: https://doi.org/10.2118/100384-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractThis paper presents a simple diagnostic tool to identify reservoir flow behavior from a Cartesian pressure/rate graph. Some of the benefits of the proposed tool are its simplicity without requiring any calculations, leading to understanding of reservoir compartmentalization and application of an appropriate material-balance technique.Data diagnosis entails graphing pressure with rate and discerning trends; positive slope signifies the pseudosteady-state (PSS) flow period, whereas the negative slope implies infinite-acting (IA) flow. Constant-rate production exhibits infinite slope whereas constant-pressure production produces zero slope. Mathematical justifications for these diagnostic signatures are presented. During PSS flow, wells belonging to the same container will exhibit the same slope.Differences in slope are an indication of reservoir compartmentalization, lateral or vertical. Equally important we provide mathematical proof of why different wells in a multiwell reservoir system should have the same slope. Field examples from multiple gas and gas/condensate systems show how the proposed tool works in practice.IntroductionWith increasing usage of permanent downhole and/or surface sensing, the need for simple diagnostics becomes imperative so that actions can be taken just in time for reservoir management. Studies have shown that motivations for real-time sensing revolve around on-time action to maximize benefits.Various analysis techniques exist to analyze production rate data for estimating in-place fluid volume and remaining reserves. These methods entail from traditional decline curve analysis, such as those offered by Arps (1945) and Fetkovich (1980) to more sophisticated techniques (Agarwal et al. 1999; Blasingame et al. 1991; Blasingame et al. 1989; Mattar and McNeil 1997) involving both flowing bottomhole pressure and rate. Most of these methods apply to single wells in volumetric reservoirs producing single-phase fluids from a fixed drainage boundary. Mattar and Anderson (2003) provide a comprehensive treatment of the pertinent methods. Analytic methods (Marhaendrajana 2005; Marhaendrajana and Blasingame 2001) have also been proposed to handle well interference in multiwell reservoirs. Gringarten (2005) showed that the reservoir-compartmentalization question can be addressed by deconvolving simultaneously measured pressure/rate data for wells across a perceived fault barrier. Multidisciplinary approach has also been reported to address the compartmentalization question (Bigno et al. 1998).Changes in well performance may often be attributed to condensate banking, reservoir subsidence, fines migration precipitating changing skin, and a host of completion and/or wellbore-lift issues, besides depletion. Our challenge is to decipher the real reason for premature production decline. In this regard, Anderson and Mattar (2004) offer a few diagnostic clues about wellbore loading and changing skin, changing well productivity, and identifying external pressure support or interference.This study offers a simple methodology to diagnose long-term well performance, especially those that are influenced by outer boundaries. In particular, whether wells belong to the same or different compartments become quite evident. Because we are solving an inverse problem, independent methods must be used to eliminate potential reservoir, wellbore, and surface flowline network issues before reaching reasonable conclusions. Mathematical proofs are presented in support of the contentions presented in this study.Theoretical ConsiderationsWhen production is initiated in a well, various flow regimes are encountered as transition from IA to PSS flow, with possible intervening transitional flow, occurs. Fig. 1 schematically depicts such a scenario on a Cartesian pwf-q graph. Of course, the size of the connected-pore volume (CPV) within a well's drainage boundary and the rate of fluid withdrawal dictate the decline rate during PSS flow in a closed system.One complicating factor during the boundary-dominated flow is a well's ever-changing outer boundaries precipitated by changing rates of neighboring wells, drilling infill wells, injecting fluids, and encroaching aquifer, to name a few. For a perspective, Fig. 1 is intended as a practical diagnostic tool in a closed system for reservoirs with significant mobility producing gas or oil, and is not intended for tight-gas reservoirs. Keywords: Drillstem Testing, Blasingame, production monitoring, well performance, PSS, drillstem/well testing, pressure rate data, Marhaendrajana, Multiwell Reservoir System, production control Subjects: Well & Reservoir Surveillance and Monitoring, Formation Evaluation & Management, Drillstem/well testing This content is only available via PDF. 2006. 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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".