Estimating the Effect of Flow Efficiency on Oil Flow Rates Using Artificial Neural Network

Abstract Frequently drilling activity causes formation damage. The damage can be estimated from the drill stem test analysis. Formation damage is indicated by a skin factor (S) with higher than 0 or flow efficiency parameter (FE) lower than 100%. Improvement of formation damage can cause an increase in flow rate. Artificial neural network (ANN) model is used to estimate the increase flowrate of productive zones on “X” Field with FE 20% to 100%. The ANN model used reservoir pressure, temperature, permeability, formation thickness, specific gravity of oil, gas to oil ratio, oil viscosity, and flow efficiency as input parameters. While the flow rate is as the output parameter. Based on predictions with the ANN model obtained the increase in flow rate varies between 1.1% to 78.0%. Besides, it is found that flowrate of the layers on the field is influenced by the product of kh, where kh is equivalent with the flowrate.

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Publication Details

Journal
IOP Conference Series Earth and Environmental Science
Published
2025-02-01
DOI
https://doi.org/10.1088/1755-1315/1451/1/012031
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

Estimating the Effect of Flow Efficiency on Oil Flow Rates Using Artificial Neural Network

Suryo Prakoso, Muhammad Taufiq Fathaddin, Andrian Sutiadi
IOP Conference Series Earth and Environmental Science
Reservoir Engineering and Simulation Methods
article

Estimating the Effect of Flow Efficiency on Oil Flow Rates Using Artificial Neural Network

Suryo Prakoso, Muhammad Taufiq Fathaddin, Andrian Sutiadi
article en

Abstract

Abstract Frequently drilling activity causes formation damage. The damage can be estimated from the drill stem test analysis. Formation damage is indicated by a skin factor (S) with higher than 0 or flow efficiency parameter (FE) lower than 100%. Improvement of formation damage can cause an increase in flow rate. Artificial neural network (ANN) model is used to estimate the increase flowrate of productive zones on “X” Field with FE 20% to 100%. The ANN model used reservoir pressure, temperature, permeability, formation thickness, specific gravity of oil, gas to oil ratio, oil viscosity, and flow efficiency as input parameters. While the flow rate is as the output parameter. Based on predictions with the ANN model obtained the increase in flow rate varies between 1.1% to 78.0%. Besides, it is found that flowrate of the layers on the field is influenced by the product of kh, where kh is equivalent with the flowrate.

IOP Conference Series Earth and Environmental ScienceVol. 1451(1)
Universitas Trisakti (ID), Jenderal Soedirman University (ID)
Openalex Percentile: Top 91%
Reservoir Engineering and Simulation Methods
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