Estimating Fluid Properties Using Surrogate Models and Fluid Database

Arief, Ibnu Hafidz; Forest, Thibaut; Meisingset, Knut Kristian

doi:10.2118/185937-ms

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…A rise in pressure induces an increment in both the viscosity and density, whereas an increase in gas content induces a decline in viscosity and density. The results corresponds to the findings of Arief et al 14 , that the prediction performance of Kriging exceeds the discussed correlations from literature. The authors reported an estimation error of 20.7 MARE%, while this study presents a higher accuracy by implementing the saturated density.…”

Section: Rbfnsupporting

confidence: 90%

“…only the input and output variables have any influence to the model 13 . The aim of the surrogate model is to replace time consuming and cumbersome simulations and experiments to predict the outcome variables of a known input set 14 .…”

Section: Surrogate Modelsmentioning

confidence: 99%

“…This section provides information about the statistical accuracy of the different surrogate models presented in the theory section. The methodology of RBFN is discussed in Chapter 5, while the algorithm of Kriging and Neural Network is provided by the work of Arief et al 14 . The following is a study related to the performance of RBFN in comparison to Kriging, Neural Network and the most accurate modified correlation models.…”

Section: Performance Of Surrogate Modelsmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Correlation Approach to Predict Viscosity of Crude Oil Systems on the NCS

Møller

Meisingset

Arief

2018

Day 1 Wed, April 18, 2018

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An accurate estimation of viscosity values is imperative for an optimal production and transport design of hydrocarbon fluids. Based on this requirement, cost efficient and reliable empirical correlation models are highly profitable. While there are numerous correlation models from literature, a consistent correlation model is still needed, as most models are inadequate to predict an accurate oil viscosity using unbiased data. This study aims to develop new and improved empirical viscosity correlations through available field measurements on the NCS. The performance of the proposed models is then studied through a comparative analysis with published correlations from literature. I would like to express my sincere gratitude to my supervisor at Statoil ASA, Knut Kristian Meisingset, for giving me the opportunity to write my Master's Thesis for Statoil ASA, his support and interest in my work have been instrumental for the quality of this thesis. Special thanks go to Ibnu Hafidz Arief for his guidance to develop the machine learning algorithms. I am exceedingly grateful for his enthusiasm throughout this semester, the feedback and discussions have been greatly appreciated. Lastly, my appreciation goes to my faculty supervisor, Professor Runar Bøe, for his valuable input, and for letting me pursue my thesis in cooperation with Statoil ASA.

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Section: Rbfnsupporting

confidence: 90%

Section: Surrogate Modelsmentioning

confidence: 99%

Section: Performance Of Surrogate Modelsmentioning

confidence: 99%

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An Improved Correlation Approach to Predict Viscosity of Crude Oil Systems on the NCS

Møller

Meisingset

Arief

2018

Day 1 Wed, April 18, 2018

Self Cite

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Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

Khamis

Fattah

2018

J Petrol Explor Prod Technol

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Laboratory experiment ideally, is the main method to obtain PVT properties of the oil and gas reservoir fluids. The alternative two methods widely used when laboratory experiments are not available are: equation of state (EOS) and empirical PVT correlations. The EOS requires lots of numerical computations based on identifying the full compositions of the reservoir fluids properties. The measurement and calculation of these properties are very expensive and time consuming. On the other hand, using of PVT correlations which are based on easily measured field data such as reservoir pressure and temperature, and gas and oil density is reliable and more economic. In this work, three artificial intelligence (AI) technique models were developed to predict the oil-gas ratio (R v ) for volatile oil and gas condensate reservoirs. Thirteen actual reservoir fluid samples (five volatile oils and eight gas condensates) covering a wide range of fluid behavior and characteristics were used. Whitson and Torp three parameters EOS were used to generate modified black oil (MBO) PVT properties that were used as a data set for model development. The MBO PVT data points were extracted for each sample using commercial PVT software at five different separator conditions. The nature of the input data was studied showing that data type is clustered. In addition, the correlations between the input parameters were checked. This preprocessed is helpful in selecting the best method to deal with the input parameters that will be fed to the developed models. According to this analysis and since the input parameters have different ranges, normalization of these parameters is vital to improve the accuracy of the models and to get the solution quickly and efficiently. Results showed that taking the log for the input parameters is the best among the other normalization techniques. The AI techniques that have been implemented in this research are; Artificial Neural Network (ANN) models, Functional Networks (FNs) and Support Vector Machines (SVMs). Models developed based on these techniques used 17,941 data points and a ratio of 70% for training, 15% for validation, and 15% for testing. To develop these models, three Matlab codes were written for each tool where the provided input data in excel format were read and prepressed before implementation. Results obtained using these techniques showed that the ANN model predicted R v with an average square correlation coefficient of 0.9999 and an average relative error of 0.15% while FNs predicted R v with an average correlation coefficient of 0.9635 and an average relative error of 27.6%. It was noted that SVMs gave the best results with an average correlation coefficient of 0.9990 and an average relative error of 0.12%. The results concluded that ANN and SVM accurately predicted such data since this type of data are clustered and these two models can handle this kind of data. The newly developed models depend only on easily obtainable parameters in the field and can have varied applications when t...

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Real Time Reservoir Fluid Log from Advanced Mud Gas Data

Arief

Yang

2020

SPE Annual Technical Conference and Exhibition

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In a recent paper (Yang et al., 2019a), we published a machine learning method to quantitatively predict reservoir fluid properties from advanced mud gas (AMG) data. This approach has clear advantages due to early access, low cost, and a continuous reservoir fluid prediction for all reservoir zones. In this paper, we demonstrate how real time reservoir fluid logs are generated and compare the results with PVT samples or production data from the same well. We develop a workflow of generating reservoir fluid logs from AMG data and PVT database. The workflow consists of two main processes; first a quality assessment of AMG data and second the computation of reservoir fluid properties (in this paper we use gas oil ratio). The entire workflow is written in python and embedded into existing commercial petrophysics softwares. The final product of the workflow are three log tracks which we call the reservoir fluid logs and those are 1) the concentration readings of the AMG data, 2) the QC metric score, and 3) the predicted GOR log. These three logs are plotted together with other standard open hole logs such as gamma ray, neutron-density, sonic and resistivity log to get a more comprehensive formation evaluation. Reservoir fluid logs derived from AMG data has two main advantages. First, it is the only approach to acquire continuous reservoir fluid properties along the well path. The continuous fluid profile can be used to understand the variation of reservoir fluids in both vertical and lateral direction. The second advantage is that the reservoir fluid log is obtained while drilling and therefore the information can be used to optimize the drilling process or the downhole sampling program during wireline operation. In this paper, we demonstrate the application of the reservoir fluid logs in four conventional field cases. In the first study case we show the benefit of using the reservoir fluid logs in a horizontal well as a substitute for downhole fluid sampling. In the second case study, we demonstrate how the reservoir fluid log is utilized to optimize the downhole fluid sampling program which results in reducing the subsurface uncertainty. Next, we exhibit the use of the reservoir fluid logs to locate gas oil contact in a case where pressure data does not show clear distinction of gas and oil gradient in the reservoir. In the last example, we illustrate the use of reservoir fluid knowledge from AMG to characterizing the fluid variation across a field. The field applications demonstrate the success of the new method for conventional reservoirs, provided good-quality AMG data are available.

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Estimating Fluid Properties Using Surrogate Models and Fluid Database

Cited by 4 publications

References 7 publications

An Improved Correlation Approach to Predict Viscosity of Crude Oil Systems on the NCS

An Improved Correlation Approach to Predict Viscosity of Crude Oil Systems on the NCS

Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

Real Time Reservoir Fluid Log from Advanced Mud Gas Data

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