Efficient Technology Following Cyclic Steam Stimulation for Thick MassiveHeavy Oil Reservoirs
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Abstract
Efficient Technology Following Cyclic Steam Stimulation for thick Massive Heavy Oil Reservoirs Shangqi Liu; Shangqi Liu PetroChina Co. Ltd. Search for other works by this author on: This Site Google Scholar Yongrong Gao; Yongrong Gao PetroChina Co. Ltd. Search for other works by this author on: This Site Google Scholar Shuangmao Liu Shuangmao Liu Research Inst. Petr. Expl/Dev Search for other works by this author on: This Site Google Scholar Paper presented at the International Oil & Gas Conference and Exhibition in China, Beijing, China, December 2006. Paper Number: SPE-104405-MS https://doi.org/10.2118/104405-MS Published: December 05 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Liu, Shangqi, Gao, Yongrong, and Shuangmao Liu. "Efficient Technology Following Cyclic Steam Stimulation for thick Massive Heavy Oil Reservoirs." Paper presented at the International Oil & Gas Conference and Exhibition in China, Beijing, China, December 2006. doi: https://doi.org/10.2118/104405-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 International Oil and Gas Conference and Exhibition in China Search Advanced Search AbstractWith high temperature and high pressure 3-D physical simulation and reservoir numerical modeling, the gravity assisted steam flooding process with the combination of vertical and horizontal wells was investigated, including its production mechanism and feasibility, the influence of key parameters and the process optimization. The results indicated that there are 3 phases: pressure-drive dominance, the combined effects of pressure-drive and gravity drainage, and gravity drainage dominance for this new technology, compared with SAGD process in oil sand. In each phase, the pressure differential and production performance have different character. The heat communication between injection and production wells has great impact on pressure difference.Using this new technology, the recovery factor can increases by 35% with cumulative oil steam ratio of 0.188 after CSS.IntroductionThe thick massive heavy oil reservoirs are main resources in Liaohe Oilfield, PetroChina, and accounting for 44.8% of total heavy oil reserves. The cyclic steam stimulation (CSS) method had been applied to recover most part of these reservoirs for up to 9 cycles and has been entered into the mid and later stage. The reservoir pressure drops greatly from initial reservoir pressure to 1.5∼2.0Mpa. The oil production per cycle decreased greatly and the stream oil ratio (OSR) declined leading to the lower recovery efficiency.Field test and reservoir simulation showed that the lower part of the formation was not swept and developed by steam due to steam override and reservoir heterogeneities, resulting in a lot of oil left in the lower part of the formation and low oil recovery factor(lower than 20%) after CSS. The major technical challenge encountered is what recovery methods following CSS could be adopted to improve recovery factor further. For this reason, many field tests with different recovery methods after CSS was conducted, such as steam flooding and hot water flooding, but the field test results are not satisfaction for many aspects.Based on lab simulation, according to the reservoir characteristics and remaining oil distribution, the gravity assisted steam drive (GASD) process with the combination of vertical and horizontal wells is a promising technology for this kind of reservoir. The main reason lies in:abundant remaining oil in inter-well area of vertical wells, and the potential reserves for further development;the thicker formation and larger gravity effect to utilize infill-drilled horizontal wells;the injected steam could heats the reservoir and decreases the oil viscosity, also provide the displacing pressure at the initial phase, preparing for continual production of heavy oil.Reservoir CharacteristicsGao 3 block locates at the nose structure of the northern part of west slope in west sak of the Liaohe downfaulted basin (Figure 1). The formation interested is Lianhua reservoir in Shahejie series of Eogene system. Reservoir depth is about 1450∼1690m, and the reservoir average net thickness is 82.2m, the oil bearing area employed is 5.0 Km2 and initial oil in place (OOIP) is 4730×104t. There are 8 sand bodies grown in the reservoir vertical direction in which the sand bodies 1–4 is gas reservoirs, the sand body 5 is oil reservoir with top gas, and the sand bodies 6 and 7 is pure oil reservoir and the oil reservoir with bottom water respectively. The initial oil gas interface is at -1510m and initial oil water interface is at -1690m. The reservoir is mainly composed of coarse conglametic sandstone and glutenite with porosity of 20∼25%, and air permeability of about 1200∼2500×10–3µm2. The viscosity of oil with solution gas is 518 mPa.s and the viscosity of stock tank oil at 50? is 2000∼3500 mPa.s. The density of surface oil is 0.94∼0.96g/cm3 . The initial solution gas and oil ratio (GOR) is 24∼39 m3/t, and the formation volume factor is 1.0624. The initial reservoir pressure is 16.1Mpa and the saturated reservoir pressure is 12.4Mpa. Keywords: Modeling & Simulation, temperature distribution, thermal method, steam-assisted gravity drainage, SAGD, enhanced recovery, Upstream Oil & Gas, reservoir pressure, oil reservoir, Oil Viscosity Subjects: Improved and Enhanced Recovery, Thermal methods This content is only available via PDF. 2006. 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".