"Predicting Reservoir Permeability: Sif Fatima Oilfield Study"

What is it about?

The study focuses on enhancing the process of history matching in reservoir modeling, a critical task for reservoir teams. This process typically involves back-and-forth iterations between geo-modelers and simulation engineers to achieve accuracy. The challenge lies in accurately incorporating data into the subsurface geological model, particularly in determining permeability, a key factor influencing fluid flow and hydrocarbon distribution in the reservoir. The objective of this study is to predict the absolute reservoir permeability in partially cored and un-cored wells using the hydraulic flow unit (HFU) concept based on flow zone indicator (FZI) distribution. To achieve this, an Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm is employed. ANFIS uses input variables from relevant well logs (gamma-ray, sonic, density, deep resistivity, and neutron porosity) along with core data to calculate FZI. The clustering analysis of predicted FZI helps characterize various reservoir units, facilitating the calculation of absolute reservoir permeability using a modified Kozeny-Carman correlation. The study's results demonstrate a strong correlation between calculated permeability and core data in OPW-1 (R2= 0.98). Extending this approach to six other wells in the Sif Fatima field in Algeria, un-cored wells utilized the ANFIS model from neighboring cored wells. Validation occurred at both well and field levels, incorporating calculated permeability into reservoir simulation models and matching bottom-hole pressure and historical production rates. Ultimately, this method proves efficient in predicting absolute reservoir permeability for un-cored sections and wells, minimizing time consumption during initial history matching. This process aids in validating the accuracy of the subsurface geological static model within dynamic models, offering a quicker and effective means of assessment.

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Photo by Steven Van Elk on Unsplash

Photo by Steven Van Elk on Unsplash

Why is it important?

This study holds significance in the realm of reservoir management and modeling for various reasons: History Matching Efficiency: The history matching process in reservoir management often involves prolonged iterations between geo-modelers and simulation engineers. Enhancing this process is crucial, and this study's approach offers a potentially more efficient method. Permeability as a Crucial Factor: Permeability plays a pivotal role in determining reservoir quality and fluid flow. Achieving history matching with minimal alterations in permeability is challenging but critical for accurately modeling hydrocarbon distribution in reservoirs. Predictive Technique: The study introduces the use of the hydraulic flow unit (HFU) concept based on flow zone indicators (FZIs) and employs an Artificial Intelligence algorithm (Adaptive Neuro-Fuzzy Inference System - ANFIS) to predict absolute reservoir permeability. This method is innovative and aims to streamline the prediction process. Validating Model Performance: The research extends its approach to multiple wells in the Sif Fatima field, validating the calculated permeability against core data. The validation process considers well bottom-hole pressure and field-wide historical performance, ensuring robustness in assessing key reservoir parameters. Time and Resource Efficiency: By enabling the prediction of absolute reservoir permeability for un-cored sections and wells, this approach potentially reduces the time required for the initial history matching process. This efficiency is crucial in expediting the validation of subsurface geological static models within dynamic models. In summary, the study introduces a novel predictive method for determining absolute reservoir permeability, aiming to streamline the history matching process. Its potential for faster validation of subsurface geological models within dynamic models could significantly impact the efficiency of reservoir management and modeling practices.

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