Dynamic failure analysis of process systems using neural networks

Adedigba, Sunday; Khan, Faisal; Yang, Ming

doi:10.1016/j.psep.2017.08.005

Cited by 75 publications

(17 citation statements)

References 35 publications

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“…Each GP model and kernel function were trained by constantly updating the hyperparameters until the best match describing the well log correlation was reached by the respective models. GP generally does not suffer from overfitting like other intelligent systems like neural networks (Adedigba et al 2017;Onalo et al 2018a). Nevertheless, overfitting can arise from the marginal likelihood optimization, especially with many hyperparameters (Mohammed and Cawley 2017;Rasmussen and Williams 2006).…”

Section: Gaussian Process Model Developmentmentioning

confidence: 99%

Data-driven model for shear wave transit time prediction for formation evaluation

Onalo

Adedigba

Oloruntobi

et al. 2020

J Petrol Explor Prod Technol

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Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynamic evaluation of reservoir formations. In the exploration and production of oil and gas reservoirs, these sonic logs contain crucial information about the formation. However, shear sonic logs are not acquired in all oil and gas exploration wells. More so, many offset wells are not run with the most recent sonic logging tools capable of measuring both shear and compressional sonic transit times due to the relatively high costs of running such equipment. And in wells where they are deployed, they are run only over limited intervals of the formation. Such wells lack continuous shear wave transit time measurements along the formation. In this study, an exponential Gaussian process model is presented. The model accurately predicts the shear wave transit times in the formations which lack reliable shear wave transit time measurements. The proposed model is developed using an array of well logs, namely depth, density, porosity, gamma ray, and compressional transit time. A Monte Carlo simulation is used to quantify the proposed model uncertainty. The shear sonic transit time predictions are used to estimate some formation deformation properties, namely Young's modulus and Poisson's ratio of a reservoir formation. The results suggest that shear transit time can be represented and predicted by Gaussian-based process model with RMSE, R 2 , and MSE of 11.147, 0.99, and 124.6, respectively. The proposed model provides a reliable and cost-effective tool for oil and gas dynamic formation evaluation. The findings from this study can help for better understanding of shear transit times in formations which do not have multipole sonic logs or where data have been corrupted while logging in the Niger Delta. List of symbolsRHOB Bulk density log (g/cm 3 ) DTCO Compressional wave travel time (µs/ft) Δt c Compressional wave travel time (µs/ft) RESD Deep resistivity log (ohm m) PHIE Effective porosity log (m 3 /m 3 ) den Electron density porosity (g/cc) fl Fluid density (g/cc) , b Formation density (g/cc) Δt fl Formation fluid compressional wave travel time (µs/ft) Δt ma Formation matrix compressional wave travel time (µs/ft) GR min Gamma ray in clean sandstone GR min Gamma ray in shale GR Gamma ray log (gAPI) GR Gamma ray log reading ma Matrix density (g/cc) MAE Mean absolute error MPE Mean percentage error Δt log Measured compressional wave travel time (µs/ft) N Neutron porosity N,fl Neutron response of the matrix Poisson's Ratio Rock porosity Vsh Shale volume DTSM Shear wave travel time (µs/ft) Δt s Shear wave travel time (µs/ft) s Sonic porosity PHIT Total porosity log (m 3 /m 3 ) E Young's modulus (MPa)

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Section: Gaussian Process Model Developmentmentioning

confidence: 99%

Data-driven model for shear wave transit time prediction for formation evaluation

Onalo

Adedigba

Oloruntobi

et al. 2020

J Petrol Explor Prod Technol

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“…Reservoir engineers could propose rational scenarios for tapping of remaining oil and long-term healthy development of oilfields combining machine learning results with numerical simulation. In previous study, numbers of machine learning methods have already been applied into petroleum industry to analyze data, find patterns and predict target variables [18]. The utilization of a machine learning method in petroleum industry often focus on two purposes.…”

Section: Introductionmentioning

confidence: 99%

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

et al. 2019

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Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

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“…Less frequently used methods to obtain the same purpose are: integer linear programming [10], Support Vector Machine [17], non-linear criterion of shear stability [23], and artificial neural networks (ANN) [1,8]. Currently, the main application area of ANN are predictive issues which aim at enhance exploitation processes by identifying [16] or classifying faults [26].…”

Section: Introductionmentioning

confidence: 99%

Application of neural reconstruction of tomographic images in the problem of reliability of flood protection facilities

Rymarczyk¹,

Kłosowski²

2018

Eksploatacja i Niezawodność – Maintenance and Reliability

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Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionThe exploitation of flood defences -such as dams and flood banks -is of key value that heavily impacts the security of people, animals and plants that lay within the area of the object. There are two main types of issues that may appear as a result of improper exploitation of such pieces of infrastructure. The first threat is a physical damage of the embankment, which may result in a breach. The other is the leakage of such a structure -in case of reservoirs that contain fluid chemical compound waste it might lead to a vast range of contamination.

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Dynamic failure analysis of process systems using neural networks

Cited by 75 publications

References 35 publications

Data-driven model for shear wave transit time prediction for formation evaluation

Data-driven model for shear wave transit time prediction for formation evaluation

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Application of neural reconstruction of tomographic images in the problem of reliability of flood protection facilities

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