Material-Balance Method for Production Rejuvenation With Horizontal Wells
Bibliographic record
Abstract
Material-Balance Method for Production Rejuvenation With Horizontal Wells Frank S. Hsieh; Frank S. Hsieh Sproule Associates Limited Search for other works by this author on: This Site Google Scholar Philip S. Kandel; Philip S. Kandel Sproule Associates Limited Search for other works by this author on: This Site Google Scholar Cecilia Vega Cecilia Vega University of Calgary Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, November 2000. Paper Number: SPE-65484-MS https://doi.org/10.2118/65484-MS Published: November 06 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Hsieh, Frank S., Kandel, Philip S., and Cecilia Vega. "Material-Balance Method for Production Rejuvenation With Horizontal Wells." Paper presented at the SPE/CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, November 2000. doi: https://doi.org/10.2118/65484-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/CIM International Conference on Horizontal Well Technology Search Advanced Search AbstractMaterial balance evaluation can be conducted at every stage of reservoir development, including production rejuvenation using horizontal wells. Material balance evaluation for a reservoir with water influx usually begins with the calculation of water influx quantities using aquifer models (the aquifer model approach). The calculated influx quantities are substituted into material balance equations that are arranged in straight-line formats. Should a linear slope be found, the original-hydrocarbons-in-place is the y-intercept. The key to a successful evaluation using the aquifer model approach lies in the selection of a representative aquifer model. Selecting a representative aquifer model is difficult due to reservoir uncertainties such as rock properties, and the size, geometry, and continuity of the aquifer. Also, influx calculations using the aquifer model approach are time-consuming because they usually require a trial and error method of solution.This paper presents a material balance equation in a form that allows for calculating water influx without the use of an aquifer model. This approach has many applications and is particularly useful for production rejuvenation projects, such as the drilling of horizontal wells.IntroductionMaterial balance evaluation is a method for evaluating the original-oil-in-place (OOIP) or the original-gas-in-place (OGIP) by analyzing the historical production and pressure data. It is commonly applied to validate the original-hydrocarbons-in-place (OOIP or OGIP) initially determined using the volumetric method. The principle of material balance can also be applied to substantiate reserves determined by decline curve analysis, as shown in this paper.Material balance evaluation is frequently complicated by water influx, often an unknown quantity, which makes it difficult to determine the net fluids production. Aquifer models are commonly used to calculate influx volumes when conducting material balance evaluations (aquifer model approach). Frequently, the lines in the material balance plot constructed using these influx volumes are of insufficient quality to determine the original-hydrocarbons-in-place with confidence. This occurs primarily because the aquifer models cannot adequately represent the actual aquifer behavior, and also because of the uncertainty in the input data. Consequently, linear regression is usually required to construct a straight line. However, it also further complicates the evaluation by resulting in a series of straight lines, one for each aquifer model selected.There are two unknowns in a material balance equation: the original-hydrocarbons-in-place and the water influx. As stated previously, when using the aquifer model approach, the influx volumes are calculated using aquifer models, and they are then used to solve for the remaining unknown, the original-hydrocarbons-in-place. This paper presents a material balance equation in a form that allows for calculating original-hydrocarbons-in-place directly (direct calculation approach) without the need for aquifer models. It eliminates the process of selecting a representative aquifer model and the time-consuming trial and error calculations associated with the aquifer model approach. Two examples are provided to illustrate the simplicity and the effectiveness of this approach.The principle of material balance has also been extended to cover rate-time decline analysis, which is a common method for determining the reserves.The direct calculation approach presented in this paper provides an effective method for determining an optimum reservoir development plan. When adequate production history is available, this method is applicable to production rejuvenation using horizontal wells. Keywords: upstream oil & gas, equation, decline analysis, reservoir simulation, production control, havlena, calculation approach, aquifer model approach, cecilia vega spe ps-cim 65484, production monitoring Subjects: Well & Reservoir Surveillance and Monitoring, Reservoir Simulation, Formation Evaluation & Management, Reserves Evaluation, Estimates of resource in place This content is only available via PDF. 2000. 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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".