Synthetic Well Log Generation Using Machine Learning Techniques

Akinnikawe, Oyewande; Lyne, Stacey; Roberts, Jon

doi:10.15530/urtec-2018-2877021

Cited by 39 publications

(18 citation statements)

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“…The overall reduced errors in Figure 10 verify the necessity of proper preprocessing for qualified training data. These results are in agreement with the results of previous studies, which mentioned the importance of data processing [4,6].…”

Section: Sensitivity Analysis For Trainingsupporting

confidence: 93%

“…Akinnikawe et al compared several machine learning algorithms (e.g., artificial neural networks (ANN), decision trees, gradient boosting, and random forest) for generation of synthetic well logs [6]. In addition, they predicted unusual logs such as the photoelectric (PE) and unconfined compressive strength (UCS) logs because the PE log is often not measured during well logging and the USC log requires an expensive core experiment.…”

Section: Introductionmentioning

confidence: 99%

“…If the goal is to construct a reliable porosity model and a target field lacks sonic log, a prediction model for sonic log should be trained with the available log data. However, previous prediction models tried to make a prediction model without considering which logs are needed for the prediction and why [2,3,6]. Therefore, a trained model required numerous well logs for the input layer and hundreds of wells were used for training [5,7].…”

Section: Introductionmentioning

confidence: 99%

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Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

Kim

Min

et al. 2020

Geofluids

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This study proposes a deep neural network-(DNN-) based prediction model for creating synthetic log. Unlike previous studies, it focuses on building a reliable prediction model based on two criteria: fit-for-purpose of a target field (the Golden field in Alberta) and compliance with domain knowledge. First, in the target field, the density log has advantages over the sonic log for porosity analysis because of the carbonate depositional environment. Considering the correlation between the density and sonic logs, we determine the sonic log as input and the density log as output for the DNN. Although only five wells have a pair of training data in the field (i.e., sonic and density logs), we obtain, based on geological knowledge, 29 additional wells sharing the same depositional setting in the Slave Point Formation. After securing the data, 5 wells among the 29 wells are excluded from dataset during preprocessing procedures (elimination of abnormal data and min-max normalisation) to improve the prediction model. Two cases are designed according to usage of the well information at the target field. Case 1 uses only 23 of the surrounding wells to train the prediction model, and another surrounding well is used for model testing. In Case 1, the Levenberg-Marquardt algorithm shows a fast and reliable performance and the numbers of neurons in the two hidden layers are of 45 and 14, respectively. In Case 2, the 24 surrounding wells and four wells from the target field are used to train the DNN with the optimised parameters from Case 1. The synthetic density logs from Case 2 mitigate an underestimation problem in Case 1 and follow the overall trend of the true density logs. The developed prediction model utilises the sonic log for generating the synthetic density log, and a reliable porosity model will be created by combining the given and the synthetic density logs.

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Section: Sensitivity Analysis For Trainingsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

Kim

Min

et al. 2020

Geofluids

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“…Generating synthetic data using other available inputs to minimise the data gaps was adopted by many researchers. Akinnikawe et al (2018) published synthetic well log generation method with ML and showed the importance of understanding input datasets and its detailed statistical behaviour. Large number of research works were also published which estimate reservoir properties using various petrophysical measurement.…”

Section: Discussionmentioning

confidence: 99%

Facies classification with different machine learning algorithm – An efficient artificial intelligence technique for improved classification

Mandal

Rezaee

2019

ASEG Extended Abstracts

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“…During the last decade, improved computing hardware and software has led to the prosperous application of machine learning (ML) in different areas of oil industry such as seismic data, petrophysical analysis including synthetic log generation or prediction [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], which has shown to be a promising tool to help address their problems in a rigorous, repeatable way. Such methods, by considering various available parameters, can give a better prediction of the missing data than simple linear methods [10,26,27].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

Khatibi

Aghajanpour

2020

Energies

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For a safe drilling operation with the of minimum borehole instability challenges, building a mechanical earth model (MEM) has proven to be extremely valuable. However, the natural complexity of reservoirs along with the lack of reliable information leads to a poor prediction of geomechanical parameters. Shear wave velocity has many applications, such as in petrophysical and geophysical as well as geomechanical studies. However, occasionally, wells lack shear wave velocity (especially in old wells), and estimating this parameter using other well logs is the optimum solution. Generally, available empirical relationships are being used, while they can only describe similar formations and their validation needs calibration. In this study, machine learning approaches for shear sonic log prediction were used. The results were then compared with each other and the empirical Greenberg–Castagna method. Results showed that the artificial neural network has the highest accuracy of the predictions over the single and multiple linear regression models. This improvement is more highlighted in hydrocarbon-bearing intervals, which is considered as a limitation of the empirical or any linear method. In the next step, rock elastic properties and in-situ stresses were calculated. Afterwards, in-situ stresses were predicted and coupled with a failure criterion to yield safe mud weight windows for wells in the field. Predicted drilling events matched quite well with the observed drilling reports.

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Synthetic Well Log Generation Using Machine Learning Techniques

Cited by 39 publications

References 0 publications

Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

Facies classification with different machine learning algorithm – An efficient artificial intelligence technique for improved classification

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

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