A Volcanic Reservoir: Facies Distribution Model Accounting for Pressure Communication
Bibliographic record
Abstract
A Volcanic Reservoir: Facies Distribution Model Accounting for Pressure Communication Tomomi Yamada; Tomomi Yamada Japan Petr. Explor. Co. Ltd. Search for other works by this author on: This Site Google Scholar Yoshiyuki Okano Yoshiyuki Okano Japan Petr. Explor. Co. Ltd. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia, April 2005. Paper Number: SPE-93159-MS https://doi.org/10.2118/93159-MS Published: April 05 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Yamada, Tomomi, and Yoshiyuki Okano. "A Volcanic Reservoir: Facies Distribution Model Accounting for Pressure Communication." Paper presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia, April 2005. doi: https://doi.org/10.2118/93159-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 Asia Pacific Oil and Gas Conference and Exhibition Search Advanced Search AbstractA TCF-class gas field has been producing over decades in Japan. The reservoir body comprises stacked Rhyolite lava domes erupted under submarine environment. Porous network developed in each dome and rapid chilling by seawater caused Hyaloclastite to deposit over it. Although Hyaloclastite is also porous in this field, its permeability has been dramatically reduced by clay minerals. These reservoir facies are interbedded with Basaltic sheets erupted alternately and Mud sedimented while the volcano was dormant.Based on stratigraphic correlation, multiple reservoirs were originally interpreted. Gas had been produced according to the priority assigned to each. However, it was noticed after 10–20 years of production that pressures of all unexploited units had been declining with variety of rates. We confirmed that by subsequent survey and decided to remodel the whole pressure system. As seismic data had not been informative, we made maximum use of the pressure data to resolve the nature of the communications.We employed multi-point geostatistics to capture the complicated facies distribution patterns. Realizations are then calibrated against pressure history through a gradual deformation technique. A common difficulty of building proper stationary training image is further pronounced in modeling a volcanic reservoir. We solved this by iteratively adjusting a training image inferred from literatures until acceptable history match was reached with reasonable number of deformations. Another issue was undetermined field extent. We settled this by stochastically populating a rectangular-parallelepiped modeling space of regular cells with pay and non-pay facies.Resulted realizations closely simulate pressure history and look realistic in both facies distribution and field extent. They ascribe the uneven pressure decline to narrow channels of Rhyolite and confinements by Hyaloclastite. Finalized training image indicates more intensity in facies spatial variation than had been expected. Based on 20 realizations, roughly 15% of scatter was estimated in OGIP around the mean.IntroductionDynamic behavior of a volcanic reservoir is closely linked to its particular process of originating porous medium. We first review its general mechanism and point out specific issues on the field of interest that are crucial in characterizing its pressure system.What is a volcanic reservoir?Volcanic lava is one of common reservoir facies hosting oil and gas accumulation in Japan. Basalt and Rhyorite lava can develop porous medium, especially if it is erupted under submarine environment. When fluid lava reaches to seafloor through a feeder channel, it undergoes rapid cooling by seawater and forms a mound of solid rock. Due to pressure drop, volcanic gas bubbles come out of solution and porous network is formed inside. Quenching and contraction in the outer layer create network of fractures. Extensive brecciation on surface of the mound generates rock fragments to deposit around it, which is called Hyaloclastite. Fig.1 presents such conceptual model for Rhyolite lava [1]. Keywords: proportion, stochastic simulation, perturbation, permeability, history, hyaloclastite, realization, upstream oil & gas, volcanic reservoir, simulation Subjects: Reservoir Characterization This content is only available via PDF. 2005. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".