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Record W4255330528 · doi:10.2118/76805-jpt

Reservoir Compaction

2002· article· en· W4255330528 on OpenAlexaffabout
A. Settari

Bibliographic record

VenueJournal of Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompactionSubsidenceGeologyGroundwater-related subsidenceNorth seaGround subsidenceWork (physics)Petroleum engineeringEngineeringMining engineeringGeotechnical engineeringOceanographyGeomorphologyStructural basinMechanical engineering

Abstract

fetched live from OpenAlex

Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Introduction Reservoir compaction has been considered an "exotic" aspect of reservoir engineering, usually studied only when the associated surface subsidence became a problem. In recent years, it has been recognized that reservoir compaction occurs in many reservoirs, and it is responsible both for improved recovery and a number of field operating problems. Consequently, the understanding of the process and the methods for its analysis have improved dramatically. This article provides a general overview of the compaction aspects of reservoir engineering. Compaction in Reservoirs In the past, reservoir compaction was usually dealt with only after being indicated by the associated surface subsidence or operational problems. Some well-known cases include the Willmington field in California and the Ekofisk field in the North Sea. Depletion of the Willmington field caused a subsidence bowl reaching a maximum depth of 9 m, requiring extensive remedial work in and around the city of Long Beach.1 The sea floor under the Ekofisk platform sank by 1984 in excess of 3.5 m, and the platform had to be extended(jacked up) at a cost of U.S. $1 billion.2 In both cases, the events triggered extensive reservoir/geomechanical studies and changes in field management to arrest further subsidence. Compaction is present in many other North Sea chalk reservoirs such as Eldfisk, Valhall, Dan, Tyra, and Gorm. Compaction of California Diatomite fields was the cause of numerous wellfailures, which in some cases reached 20 to 30% of the wellsdrilled.3 Better understanding of the cause of the deformations led to a significant decrease in well failures. Compaction also has been recognized as an important drive mechanism in Alberta and Venezuela heavy oils and oil sands. In this case, the problem is further complicated by thermal aspects and by the unconsolidated nature of the porous media. Recent exploration activity tends to discover more and more deepwater "soft" reservoirs (e.g., in the Gulf of Mexico) and high-temperature/high-pressure reservoirs, where compaction often is an important issue. Compaction of the reservoir itself, besides providing the additional drive energy for production (in some cases amounting to 50 to 80% of total energy), has important consequences both inside and outside the reservoir. The most obvious is the surface/seafloor deformation (i.e., subsidence), which creates problems for the environment as well as for oilfield structures and seabed pipelines. Additional problems include well failure caused by casing deformations, fault reactivation resulting in seismic activity (e.g., Groningen gas field in the Netherlands), reduction in permeability leading to loss of productivity, and effect of deformation on overlying shales or freshwater aquifers.4 While compaction can contribute to reservoir energy and increase recovery, its side effects are undesirable: increasing development costs and creating barriers to project acceptance. Therefore, field development of compacting reservoirs is always more complex compared with conventional reservoirs, and it requires a more detailed analysis. Inaccurate estimate of the compaction effect can lead to over- or underestimation of reserves, even in gas reservoirs. Compaction/subsidence problems may be expected in "soft" or unconsolidated formations and chalk reservoirs, which are overpressured or will be severely depleted and have large thickness. Because most deformations are irreversible, it is difficult to correct the negative effect after compaction has occurred. This critical issue points to the need to screen, assess, and engineer for compaction early in the life of a field. As deeper reservoirs are developed in deeper water with fewer, very expensive wells, there is an increased need to improve the engineering and modeling technology to deal with these difficult issues at the planning stage. Compaction Mechanisms While all reservoirs undergo deformations during exploitation, compaction is the process in which the compressive strength of the rock is exceeded and plastic deformation occurs, resulting in irreversible reduction of porosity and permeability. This irreversible change differentiates compaction from elastic compression of the reservoir. The volumetric behavior of the rock under plastic deformation determines the changes in porosity and apparent compressibility.Fig. 1a shows the customary representation of the process in which porosity, f, is a function of pressure, p. However, from the theory of poroelasticity5 (and thermoelasticity), the volumetric behavior of the rock is fundamentally a function of the mean effective stresssm'=sm-ap, wheresm is the mean total stress, sx=(sy+ sz)/3; a is Biot's constant; and p is the fluid (pore) pressure. The porosity vs. effective stress (also called net pressure) during the depletion of the reservoir follows the path shown in Fig. 1b. It must be stressed that the fundamental physical mechanism is that of Fig. 1b, while the representation in Fig. 1a is an approximation, which must be derived from data of Fig. 1b by ignoring (or making assumptions about) stress variations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.011

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.

Opus teacher head0.010
GPT teacher head0.210
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2002
Admission routes2
Has abstractyes

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