A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

Elkatatny, Salaheldin; Moussa, Tamer; Abdulraheem, Abdulazeez; Mahmoud, Mohamed

doi:10.3390/en11123490

Cited by 21 publications

(7 citation statements)

References 33 publications

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“…The most employed feature is S, exclusively used in [26,[49][50][51]; P and Q were employed in [52,53]. In a recent work [54] P and V RMS measurements were used, sampled at 1 Hz, obtaining a high level of accuracy, even with varying supply voltages.…”

Section: Feature Setsmentioning

confidence: 99%

“…There are many ANN applications of ANN in the field of drilling fluid in the last few years. Some of these researches are the prediction of filtration volume and mud cake permeability of water-based mud (WBM) [40], drill cutting settling velocity prediction [41], prediction of differential pipe sticking [42], lost circulation prediction [43], hole cleaning efficiency of foam fluid [44], rheological properties of invert emulsion mud [45], invert emulsion mud rheology [46] and spud mud rheology prediction [47], generating geomechanical well logs [48], prediction of oil PVT properties [49].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

2020

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The world has begun to focus on migrating energy systems and applications in a smart and sustainable manner. Environmental experts warn that the average temperature increase of the sea by 2 degree Celsius may lead to irreversible effects on ice melting and global warming. Government officials may face economic growth and carbon reduction dilemmas. Whilst continuing to utilize fossil fuel as the main energy source, artificial intelligence was introduced to improve existing energy systems and applications. Oweing to the advancement of big data infrastructure and high-performance computing tools, data collection via energy network sensing devices provides ground truth and valuable information for further processing and analysis.This edition aims to share the latest research and results on artificial intelligence in energy systems and applications for smart and sustainable systems. Special attention is drawn to non-intrusive load monitoring, electric grids, wireless sensor networks, insulator detection, rheological property prediction and static Young's modulus estimation.Many countries have already replaced traditional electric grids with smart grids by installing smart meters. In general, advanced metering infrastructure is set up to support two-way communication between end users and smart meters. With the total electricity consumption of rooms and buildings, the benefits of smart metering are limited. Therefore, the research problem of non-intrusive load monitoring (also named electricity disaggregation) from total electricity consumption to the electricity breakdown of individual appliances has been a key research direction for smart metering. Recently, granular, or high frequency electricity, data, and deep learning have become feasible approaches to escalate the performance of electricity disaggregation.Attention has been drawn to the application of artificial intelligence techniques for electric grid and wireless sensor networks. Electric grid models for the prediction of peak-demand and tariff have been proposed. Wireless sensor networks are often linked with traditional wired networks to allow for mobility and quick installation. However, energy efficient schemes should be deployed to increase the lifespan of wireless sensors.Artificial intelligence has been applied and its effectiveness evaluated as a way to improve the existing software, hardware, algorithms, tools, systems, and applications. The results may drive new insights that have not been realized by human beings. This collection has gathered works on insulator detection in inspection imaging, rheological property prediction for CaCl 2 brine-based drill-in fluid, and static Young's modulus estimation for sandstone formation. In the last decade, we have witnessed the success and effectiveness of applying artificial intelligence to energy systems and applications. We expect that energy research will continue to grow worldwide and will contribute to the vision of a smart city. In particular, edge computing, fog computing, and cloud computing will matu...

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Section: Feature Setsmentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

2020

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

et al. 2019

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Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using M W and M F measurements then extract empirical correlations in a white-box mode to predict these properties based on M W and M F . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.

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“…The use of machine learning in the oil industry is fast growing in various sectors. These applications include but are not limited to estimation and optimization of drilling parameters 13 – 18 , drilling fluid properties 19 – 21 , reservoir fluid properties 22 – 27 , petrophysical properties 28 – 32 , and geomechanical properties 33 – 36 . Different models between static and dynamic Poisson’s ratio were developed using different machine learning methods such as an artificial neural network (ANN), Fuzzy Logic (FL), Functional Network (FN), and Alternating Conditional Expectation (ACE) as presented in Table 2 .…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools

Siddig

Gamal

Elkatatny

et al. 2021

Sci Rep

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Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns of these methods are the data and samples availability, costs, and time-consumption. In this paper, an alternative real-time technique utilizing drilling parameters and machine learning was presented. The main added value of this approach is that the drilling parameters are more likely to be available and could be collected in real-time during drilling operation without additional cost. These parameters include weight on bit, penetration rate, pump rate, standpipe pressure, and torque. Two machine learning algorithms were used, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To train and test the models, 2905 data points from one well were used, while 2912 data points from a different well were used for model validation. The lithology of both wells contains carbonate, sandstone, and shale. Optimization on different tuning parameters in the algorithm was conducted to ensure the best prediction was achieved. A good match between the actual and predicted Poisson’s ratio was achieved in both methods with correlation coefficients between 0.98 and 0.99 using ANN and between 0.97 and 0.98 using ANFIS. The average absolute percentage error values were between 1 and 2% in ANN predictions and around 2% when ANFIS was used. Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.

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A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

Cited by 21 publications

References 33 publications

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools

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