Machine Learning Leads Cost Effective Intelligent Fluid Design: Fluid Engineering Perspective

Shadravan, Arash; Tarrahi, Mohammadali

doi:10.2118/180033-ms

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…With intelligent fluid design, thousands of hours of laboratory testing are saved. The developed prediction model is able to successfully extract and relate input parameters to output variables[25].Cao et al introduced…”

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confidence: 99%

A data-driven smart proxy model for a comprehensive reservoir simulation

Alenezi

Mohaghegh

2016

2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT)

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The preferred common tool to estimate the performance of oil and gas fields under different production scenarios is numerical reservoir simulation. A comprehensive numerical reservoir model has tens of millions of grid blocks. The massive potential of existing numerical reservoir simulation models have gone unrealized because they are computationally expensive and time-consuming. Therefore, an effective alternative tool is required for fast and reliable decision making. To reduce the required computational time, proxy models have been developed. Traditional proxy models are either statistical or reduced-order models (ROM). They were developed to substitute complex numerical simulation with producing a representation of the system at a lower computational cost. However, there are shortcomings associated with these approaches when applied to complex systems. In this study, a novel proxy-model approach is presented in order to overcome the computational size and the traditional proxy-model challenges. The smart proxy model presented is based on artificial intelligence and data-mining techniques. The objective of this study was to develop two types of smart proxy models at each grid block. The first smart proxy model was generated to identify dynamic reservoir properties (pressure and saturations). The other proxy model was created to determine the production profile of a well. The two smart proxy models can be coupled in order to examine field production performance under different operational and geological realization. The field of study in this work is the SACROC unit. It is a depleted oil field located in Scurry County, Texas. The production history of this field began back in the late 1940s. Based on the long period of production and the different drive mechanisms employed throughout the fields exploitation, its performance history was divided into three phases in this study. Each phase was investigated and smart proxy models were applied to each. ful children, Jana, Naif and Reelam, who provide unending inspiration. Finally, I extend my thanks and gratitude to Saudi Aramco for giving me the opportunity and financial support to pursue a PhD degree.

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confidence: 99%

A data-driven smart proxy model for a comprehensive reservoir simulation

Alenezi

Mohaghegh

2016

2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT)

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Intelligent Tool To Design Drilling, Spacer, Cement Slurry, and Fracturing Fluids by Use of Machine-Learning Algorithms

Shadravan

Tarrahi

Amani

2017

SPE Drilling &Amp; Completion

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Summary Design of drilling fluids, spacers, cement slurries, and fracturing fluids is often done by trial and error in the laboratory. In the first step, the required properties of these fluids are categorized and then efforts will be started with a rough idea of the optimal composition. This first guess usually depends on the experience of the laboratory analyst or fluid engineer. Afterward, the trial-and-error testing starts, and it continues until the fluid design moves closer to the desired fluid criteria. There are several test data that would not be used in this method, and it is difficult to digest a large amount of information by the user. Trial and error could be time-consuming, very costly, and misleading. Today, there is a need for an intelligent system that uses all the available data (big data), even if the data sets are not close to the desired goal, and offers insights for fluid designs. This paper conducted a study on the application of machine-learning-based methodologies, including Gaussian-process regression (GPR) and artificial neural networks (ANNs), to reduce the costs of testing, integrate available experimental data, and eliminate the need for personnel supervision. These practical nonlinear-regression methods empower efficient and fast prediction tools that do not require including complex physics of the underlying system while integrating all available data from different sources. GPR, which is also known as Kriging in geostatistics literature, has exceptional advantages over traditional regression methods because it does not require a known form for regression function and also has the capability of determining the estimation error and the confidence interval. This machine-learning-based tool offers insights for intelligent fluid design and could reduce costs.

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A Thorough Review of Machine Learning Applications in Oil and Gas Industry

Temizel

Canbaz

Palabiyik

et al. 2021

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one.

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Machine Learning Leads Cost Effective Intelligent Fluid Design: Fluid Engineering Perspective

Cited by 5 publications

References 18 publications

A data-driven smart proxy model for a comprehensive reservoir simulation

A data-driven smart proxy model for a comprehensive reservoir simulation

Intelligent Tool To Design Drilling, Spacer, Cement Slurry, and Fracturing Fluids by Use of Machine-Learning Algorithms

A Thorough Review of Machine Learning Applications in Oil and Gas Industry

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