Permeability Determination of the PL19-3 Field for Geologic Model Input
Bibliographic record
Abstract
Permeability Determination of Bohai Bay Field for Geologic Model Input Michael David Fetkovich; Michael David Fetkovich ConocoPhillips Search for other works by this author on: This Site Google Scholar Matt Gerard; Matt Gerard ConocoPhillips Search for other works by this author on: This Site Google Scholar Lee Chin; Lee Chin ConocoPhillips Search for other works by this author on: This Site Google Scholar ShuXing Dong ShuXing Dong ConocoPhillips Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, June 2006. Paper Number: SPE-100307-MS https://doi.org/10.2118/100307-MS Published: June 12 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Fetkovich, Michael David, Gerard, Matt, Chin, Lee, and ShuXing Dong. "Permeability Determination of Bohai Bay Field for Geologic Model Input." Paper presented at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, June 2006. doi: https://doi.org/10.2118/100307-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Europec featured at EAGE Conference and Exhibition Search Advanced Search AbstractThe overall structure of the PL19–3 field, which is located in Bohai Bay, is an asymmetrical wrench anticline that formed by a combination of differential subsidence, strike-slip faulting, and normal faulting. Faults form the main trapping components for the individual hydrocarbon-bearing fault blocks. The reservoir sands of the deeper Guantao formation are dominantly braided fluvial sandstones, while the shallower Minghuazhen formations are dominantly meandering fluvial sands. Fault blocks penetrated by appraisal or development wells tested oil with viscosities ranging from 10 to 380 cp. Multiple vertically separate pressure systems exist in each fault block.The purpose of this paper is to present a case history that demonstrates multiple methods used to calculate permeability for input into a fine grid geologic model and ultimately a flow simulation model. Special core analysis and facies description was used to generate facies-based permeability versus porosity relationships that can be used with log-calculated variables. Permeability log curves were calculated for each well in the field, and then input them into a geologic flow model. Pressure transient analysis was used to condition the facies based porosity versus permeability relationships to ensure that they matched actual well performance.The permeability logs for all of the producing and injection wells were input into a flow simulation model. Comparisons of model predicted versus actual performance show close agreement. A good permeability estimate ultimately results in reasonable values of transmissibility, original oil in place, and sand connectivity. Advanced decline curve analysis was used as an additional method for calculation of kh, skin, and original oil in place (OOIP) and to validate model predicted performance matched actual transient, depletion, and waterflood performance behavior. In addition because this field contains reservoirs with unconsolidated sands the effect of stress dependent permeability was studied.IntroductionThe purpose of this paper was to show how all data including core, PVT, well logs, pressure transient tests, and well performance can be used to obtain a valid well log derived porosity-permeability relationship. That relationship then can be used to provide sand sequence variograms of permeability from well logs. If the relationship is valid then the model transmissibility and volumes should give forecasts that match actual performance within an acceptable range.An accurate estimate of permeability is one of the most important requirements for reservoir characterization. Permeability has a large influence on reservoir connectivity, recoverable reserves, decline behavior, individual well productivities, and waterflood behavior. To generate reliable production forecasts for reserves, budgets, well location placement, and development strategies, valid values of permeability are input into a reservoir flow model. The three methods used to ensure valid flow model permeability input are core analysis, Pressure Transient (PT) analysis, and Advanced Decline Curve (ADC) analysis. All three methods should give similar values of permeability.In the following sections, the methods used to determine permeability and the engineering evaluation of determined permeability are presented in detail. First, the procedure employed for establishing the relationship between core-based porosity and permeability was described. Second, the effect of stress dependency in permeability on well production behavior was evaluated. If the impact of stress dependency in permeability on well production behavior is insignificant, then there is no need to consider the influence of stress on permeability determination in PT, ADC analyses, and reservoir flow simulation. Third, the method and results of pressure transient analysis for determining permeability are described. Fourth, the detailed description and results of ADC analysis to determine permeability are presented. Fifth, verification of permeability determined by the methods presented in the paper was performed by full-field reservoir flow simulation. Finally, conclusions from this study are provided.Results and DiscussionCore Based Porosity-Permeability Relationship.Conventional cores were cut from three appraisal wells. In addition, percussion sidewall cores were taken in each of the eight wells drilled during field discovery and appraisal. Keywords: Upstream Oil & Gas, Modeling & Simulation, flow in porous media, Fluid Dynamics, flow rate, drillstem/well testing, Well Productivity, recovery factor, Permeability Determination, stress-dependent permeability Subjects: Reservoir Fluid Dynamics, Formation Evaluation & Management, Flow in porous media, Drillstem/well testing This content is only available via PDF. 2006. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".