Indonesia’s Unconventional Resources, Modified Resource Triangle, and a Typical Example of Stimulation of Coalbed Methane Reservoir
Bibliographic record
Abstract
Indonesia has been producing oil since 19th century and started producing 2 types of Unconventional Resources (UR) which are heavy oil since 1958 and Coal Bed Methane (CBM) since this year (2012). The concept of resource triangle was introduced by Masters and Gray in 1979 to describe the huge reserves of UR and its difficulty to be produced. A modified version had been done with a scale to exactly describe the amount of oil or gas reserve of UR. It is then linked with the stimulation techniques needed to produce them. To recover the heavy oil, thermal stimulation or recovery technique is needed to reduce its very high viscosity. Similarly, Coal Bed Methane (CBM) is also categorized as unconventional reservoirs (UR) because of its low permeability and can only be produced economically at commercial flow rates with special recovery processes or well-stimulation treatment. This paper will give a comparison study of producing CBM with and without hydraulic fracturing, a type of well-stimulation technology. The optimization procedure to select the type of water frac fluid and propping agent size and type that technically and economically suitable for CBM reservoir. The process was done using fracturing simulator and reservoir simulator for dual porosity system (CMG GEM). Fracturing simulator predicted the amount of fluid and propping agent and the resulting fracturing conductivity. Afterwards, the post-fracturing results will then be transferred to CMG-GEM, by which, using Warren and Root equation, the gas production profile is predicted and compared with its pre-fracturing results. From this study, optimum hydraulic fracture treatments is found by having the connected permeability around 5-10 Darcy. This can be accomplished by pumping enough volumes of proppant at low concentrations (6 ppa) into the fracture using less expensive fluids and distribute it uniformly.
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 | 0.001 | 0.000 |
| 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".