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Record W2071561756 · doi:10.2118/172493-ms

Production Optimization of Heterolithic Reservoirs through Completion Strategy and Reservoir Management: A Case Study of Cream Field

2014· article· en· W2071561756 on OpenAlexaboutno aff
Asmau Nayagawa, Kefe Amrasa, Olukayode Ayeni, Abdul-Wahab Sa'ad, Olaseni Osho

Bibliographic record

VenueSPE Nigeria Annual International Conference and Exhibition · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyFluvialTransgressiveOil shalePaleontologyStructural basinPetrologySedimentary depositional environment

Abstract

fetched live from OpenAlex

Abstract Reservoir quality in terms of Net-to-Gross (NTG) remains one of the critical components in determining the Hydrocarbon-initially-In-Place (HCIIP), recoverable reserves and production rates of any producing field. Often times, fluvial channel and shoreface deposits are credited to have very good reservoir qualities, hence are choice candidates for completions post-drill of the well. In addition, examples exist of heterolithic sands from which considerable reserves have been recovered during the life-cycle production of the Cream Field in the Niger Delta basin, Nigeria. Improved production from these reservoirs is associated with optimization of well designs. Heterolithic deposits are made up of inter-bedded sand and mud/shale. These deposits are typically laid down in environments like the tide dominated deltaic and estuarine environments as found in the Niger Delta of Nigeria. The Heterolithic sands found in the field to be discussed are mainly lower shoreface sands with lesser transgressive sand units; lower energy, variably sorted sandstones which are typically finely laminated and commonly intensely bioturbated. There is a continuous transition between heterolithic and shoreface sands. Reservoir quality tends to increase upwards as the heterolithic sands grade into shoreface sands. The sands have poor Kv/Kh values due to presence of shale laminates within the sand deposits. This exacerbates the poor sweep efficiency of the oil with high possibility of by-passed oil. The overall impact of these challenges is low recovery factors assigned to the sands. Due to the properties and nature of the heterolithic sands mentioned above, there is usually low pressure support due to poor aquifer connectivity as a result of the depositional environment, thus triggering a depletion drive mechanism. Interestingly, some of these heterolithics hold considerable recoverable volume that makes the exploitation of such reserves important. Such is the case offshore Norway, Alaska, Canada, Venezuela, Russia, Nigeria and indeed world-wide. As a result, production optimization therefore becomes critical to maximize recovery from wells completed on this facie type. The paper reviews the occurrence of this heterolithics in a field in the Niger Delta, the challenges faced with the current completion strategy and the reservoir management practices. A major challenge as observed in conventional crestal completion on the structure is early gas breakthrough from secondary gas cap formation. Methods of enhancing recovery from heterolithics using improved completion strategy and the requisite reservoir management practices are set forth in the body of the paper. Completion strategies like horizontal wells targeted at the good quality sands has shown an additional potential 1300bopd (seen in the performance of the only horizontal well in the field) as compared to performance of conventional wells, simulation study of water injection and gaslift has also indicated an increase in reserves by 10MMstb.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.308
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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