New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Gowida, Ahmed; Elkatatny, Salaheldin; Alafnan, Saad; Abdulraheem, Abdulazeez

doi:10.3390/su12020686

Cited by 28 publications

(6 citation statements)

References 39 publications

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“…As for well log generation, cross‐plot and multiple regression have been applied to synthesize well logs (J. Wang et al, 2016). Some machine learning methods, such as support vector machine (Gowida et al, 2020) and random forest (Akinnikawe et al, 2018), are proposed to generate well logs, as well. Among all of these methods, neural networks have received special focus since they can build extremely complex mapping between inputs and outputs.…”

Section: Introductionmentioning

confidence: 99%

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Chen

Zhang

2020

Geophysical Research Letters

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In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small data set and process sequential data. The EnLSTM is built by combining the ensemble neural network and the cascaded LSTM network to leverage their complementary strengths. Two perturbation methods are applied to resolve the issues of overconvergence and disturbance compensation. The EnLSTM is compared with commonly used models on a published data set and proven to be the state-of-the-art model in generating well logs. In the case study, 12 well logs that cannot be measured while drilling are generated based on the logs available in the drilling process. The EnLSTM is capable of reducing cost and saving time in practice. Plain Language Summary A novel neural network, called EnLSTM, is proposed by combining the ensemble neural network, which has good performance on small-data problems, and the cascaded long short-term memory network, which is effective at processing sequential data. The EnLSTM's capability of processing sequential data based on a small data set is especially suitable for generating synthetic well logs. In addition, two perturbation methods are used to ensure that the EnLSTM can be fully trained in practice. In the experiments, the EnLSTM achieved the current best results on a published well log data set, and its application value is verified in a case study.

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Section: Introductionmentioning

confidence: 99%

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Chen

Zhang

2020

Geophysical Research Letters

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“…The FN and RF techniques were nominated for using since they were recently applied in oil and gas industry [ 34 , 44 , 51 ] with encouraging outputs and simplicity.…”

Section: Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling

Ahmed

Elkatatny

Alsaihati

2021

Computational Intelligence and Neuroscience

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The prediction of continued profile for static Poisson’s ratio is quite expensive and requires huge experimental works, and the discontinuity in the measurement and the limited applicability and accuracy of the present empirical correlations necessitated the utilization of artificial intelligence with its prosperous application in oil and gas industry. This work aims to construct different artificial intelligence models for predicting static Poisson’s ratio of complex lithology at real time during drilling. The functional networks (FN) and random forest (RF) approaches were utilized using the mechanical drilling parameters as inputs. This study uses a vertical well with 1775 records from complex lithology containing shale, sand, and carbonate for model building. Besides, a different dataset from another well was used to check the models’ validity. The results demonstrated that both FN- and RF-based models predicted static Poisson’s ratio with significant matching accuracy. The FN technique results’ correlation coefficient (R) value of 0.89 and average absolute percentage error (AAPE) values of 10.23% and 10.28% in training and testing processes. While the RF technique is outperformed, as illustrated by the highest R values of 0.99 and 0.94 and the lowest AAPE values of 1.89% and 5.19% for training and testing processes, the robustness and reliability of the developed models were confirmed in the validation process with R values of 0.94 and 0.86 and AAPE values of 11.23% and 5.12% for FN- and RF-based models, respectively. The constructed models developed a basis for inexpensive static Poisson’s ratio prediction in real time with significant accuracy.

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“…Hence, numerous ANN models were developed for real-time applications, such as estimating the RPO and the drilling performance (Ahmed et al 2019;Arabjamaloei and Shadizadeh 2011). Gidh et al (2012) improved the drilling performance by predicting and managing the bit wear utilizing an artificial neural network technique. A new ANN-based system was developed to predict the bit performance at different ROP values.…”

Section: Drilling Performance Predictionmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

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101

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Cited by 28 publications

References 39 publications

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling

A systematic review of data science and machine learning applications to the oil and gas industry

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