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Record W2038162464 · doi:10.2118/08-10-14

A New Method Using Wellhead Measurement to Approximate Unsteady- State Gas-Water Two-Phase Flow in Wellbore to Calculate Inflow Performance

2008· article· en· W2038162464 on OpenAlexaff
Qing Lin, L. Zhang, Yu‐Pin Lin, Nan Xie

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsWellheadArtificial liftInflowPetroleum engineeringGas liftMechanicsLift (data mining)SlippagePressure dropFlow (mathematics)Volumetric flow rateWellboreGeologyEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract An accurate analysis of the pressure and water inflow at the bottomhole of a gas well with free water influx is important for determining an optimized artificial lift method. The commonly used two-phase flow correlations based on steady-state flow have been successfully applied to gas wells in homogeneous reservoirs. However, for reservoirs with a high degree of heterogeneity, such as fractures, the existing techniques often fail due to the unsteady-state flow behaviour from reservoir to wellbore. This paper presents a new method that is derived based on the unsteady-state two-phase flow phenomena and the conservation of mass, taking into consideration the slippage effect of the two phases. In this method, the fluid flow is divided into a number of segments at which pressure drop and gas-liquid distribution under unsteady-state flow conditions can be calculated utilizing the measured data at the wellhead. In this case, more representative bottomhole pressures and water inflow rate changes with time can be calculated, permitting the selection of an optimum artificial lift method. The new method has been successfully tested. A field case is presented involving a gas well in the Sichuan Field in China. The results of the new calculation method and the steady-state calculations were compared with field measurements to determine the accuracy of the method. Introduction The total amount of gas that can be produced from a gas well with free water influx largely depends on its ability to lift water. Sometimes, it is necessary to engage artificial lift to dewater the well. Accurate forecasting of gas well deliverability requires accurate predictions of pressure loss and gas and liquid inflow performance to select and design the appropriate artificial lift method. Most often, two-phase flow correlations are employed to determine flow rate and pressure loss in gas wells. Those methods are generally adequate for wells completed in homogeneous reservoirs that exhibit steady-state flow. But these correlations typically fail in reservoirs with a high degree of heterogeneity, such as fractures, due to the unsteady-state flow behaviour from the reservoir to the wellbore. The modelling calculation presented in this paper outlines a method using wellhead measurements to calculate sandface pressure and flow rate in order to design artificial lift systems to manage water loading problems and improve production and ultimate recovery. An example of the application of this method for a gas lift design is included in this paper. Theoretical Development Gas flows intermittently with water from a fractured formation with free water influx. The intermittent flow is maintained in the reservoir into the wellbore. By studying gas-water unsteady-state flow in the tubing, it is possible to estimate fluctuations along the entire flow path from the sandface to the wellhead. The production of gas and water at the wellhead is not synchronized with the sandface. This is primarily due to wellbore storage effects. The slippage between water and gas under unsteady-state flow conditions causes time dependent flow rate changes to the gas and liquid phases as the gas arrives at the wellhead.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.283
Teacher spread0.256 · 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.

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

Citations3
Published2008
Admission routes1
Has abstractyes

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