Hydrocarbon Effect Correction on Porosity Calculation from Density Neutron Logs Using Volume of Shale in Niger Delta

The prediction of rock porosity and permeability is crucial for assessing reservoir productivity and economic feasibility. However, traditional methods for obtaining these properties are time-consuming and expensive, making them impractical for comprehensive reservoir evaluation. This study introduces a novel approach to efficiently predict rock porosity and permeability for reservoir assessment by leveraging real-time machine learning models. Utilizing readily available drilling parameters, this approach offers a cost-effective alternative to traditional time-consuming methods to predict formation petrophysical parameters in real-time. The data set used in this study was collected from two vertical wells located in the Middle East. It encompasses drilling parameters such as the rate of penetration (ROP), gallons per minute (GPM), revolutions per minute (RPM), strokes per minute (SPP), torque, and weight on bit (WOB), along with the corresponding measurements of porosity (ϕ) and permeability (k) obtained through core analysis. Three machine learning models, namely, decision trees (DTs), random forest (RFs), and support vector machines (SVMs), were employed and evaluated for their effectiveness in predicting porosity and permeability. The results demonstrate promising performance across the different data sets. All three models achieved correlation coefficients (R) higher than 0.91 in predicting porosity. The RF model exhibited accurate predictions of permeability, achieving R values surpassing 0.92 in the various data sets. While the DT model displayed slightly lower performance, with the R-value decreasing to 0.88 in the testing data set, the SVM model suffered from overfitting, with R values dropping to 0.83 in the testing data set. The novelty of this work lies in the successful application of machine learning models to the real-time prediction of reservoir properties, providing a practical and efficient solution for the oil and gas industry. By achieving correlation coefficients exceeding 0.91 and showcasing the models' efficacy in a dynamic testing data set, this study paves the way for improved decision-making processes and enhanced exploration and production activities. The innovative aspect lies in the utilization of drilling parameters for timely and cost-effective estimation, transforming conventional reservoir evaluation methods.

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Integration of 3D Seismic in the Absence of Core Data to Mitigate Porosity Uncertainty in a Deep, Over Pressure Reservoir in Niger Delta

Basak

Abdul-Kareem

Anyaehie

et al. 2014

All Days

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Determining reservoir porosity from density logs is possibly the most common practice in the industry over the years. Computation of porosity from density is largely affected by the grain density and fluid density used in the models. There are many best practices available in the industry to compute the grain density and fluid density with reasonable certainty. There are also many different models available which takes into account other available log responses like neutron porosity, resistivity, and shale volume to account for the uncertainties in the porosity computation. However, one of the areas that need some special attention is to integrate non-petrophysical data like seismic which can help reducing the porosity uncertainty significantly especially when no other calibration reference is available. Log measurements from the padded tools like density, neutron porosity are generally quality checked against caliper and other lithology indicators like gamma ray, resistivity, sonic etc. This paper demonstrates that porosity computation could also go wrong in a true gauge hole where density log reading apparently looks good and also showing right kind of deflection for changes in lithology. The situation can get quite complicated if the reservoir is highly over pressured and if there is no core data or regional data available. In a deep, over pressured reservoir in the niger delta, with only two well penetrations, no core measurements, and no regional calibration reference, top quality recent 3D seismic data was integrated with the well data to mitigate uncertainties in reservoir porosity. The formation evaluation challenge was that log in one of the wells showed a relatively low density reading (hence high porosity) compared to the second well density over the same reservoir. However seismic amplitude did not support the porosity variation between the two wells hence leading to further investigation on identification and management of the observed uncertainties. This paper showcased in details how the seismic data was used to identify the wrong well log reading that lead to wrong prediction of porosity distribution and the accuracy of the integrated seismic and well log predicted properties when compared to actual well log result from a well that was later drilled in the same reservoir.

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Hydrocarbon Effect Correction on Porosity Calculation from Density Neutron Logs Using Volume of Shale in Niger Delta

Cited by 4 publications

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Prediction of Formation Permeability While Drilling: Machine Learning Applications

Prediction of Formation Permeability While Drilling: Machine Learning Applications

Real-Time Prediction of Petrophysical Properties Using Machine Learning Based on Drilling Parameters

Integration of 3D Seismic in the Absence of Core Data to Mitigate Porosity Uncertainty in a Deep, Over Pressure Reservoir in Niger Delta

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