Modeling and Optimizing Rate of Penetration Using Intelligent Systems in an Iranian Southern Oil Field (Ahwaz Oil Field)

Arabjamaloei, R.; Shadizadeh, Seyed Reza

doi:10.1080/10916460902882818

Cited by 73 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANN is the most used AI technique among all AI methods because an empirical correlation can be extracted from the optimized ANN model. 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.…”

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

101

View full text Add to dashboard Cite

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.

show abstract

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

101

View full text Add to dashboard Cite

show abstract

“…Arabjamaloei [19] used ANN to build a predictive model for ROP. He used 330 data records of ten input parameters including rotary speed, weight on bit, flow rate, mud density, viscosity, depth, bit size, bit hours, bit efficiency and annulus pressure.…”

Section: Application Of Ai In Rop Predictionmentioning

confidence: 99%

New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation

et al. 2019

View full text Add to dashboard Cite

Rate of penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the petroleum industry, most of the well cost is taken by the drilling operations. Therefore, it is very crucial to drill carefully and improve drilling processes. Nevertheless, it is challenging to predict the influence of every single parameter because most of the drilling parameters depend on each other and altering an individual parameter will have an impact on the rest. Due to the complexity of the drilling operations, up to the present time, there is no reliable model that can adequately estimate the ROP. Artificial intelligence (AI) might be capable of building a predictive model from a number of input parameters that correlate to the output parameter. A real field dataset, of shale formation, that contains records of both drilling parameters such as, rotation per minute (RPM), weight on bit (WOB), drilling torque (τ), standpipe pressure (SPP) and flow pump (Q) and mud properties such as, mud weight (MW), funnel and plastic viscosities (FV) (PV), solid (%) and yield point (YP) were used to predict ROP using artificial neural network (ANN). A comparison between the developed ANN-ROP model and the number of selected published ROP models were performed. A novel empirical equation of ROP using the above-mentioned parameters was derived based on ANN technique which is able to estimate ROP with excellent precision (correlation coefficient (R) of 0.996 and average absolute percentage error (AAPE) of 5.776%). The novel ANN-based correlation outperformed three published empirical models and it can be used to predict the ROP without the need for artificial intelligence software.

show abstract

“…Artificial neural network (ANN) is computational method derived from the biological neural network [26,27]. In the architecture of ANN, the input and the output are connected by specific neurons.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

Reservoir fluid properties such as bubble point pressure (Pb) and gas solubility (Rs) play a vital role in reservoir management and reservoir simulation. In addition, they affect the design of the production system. Pb and Rs can be obtained from laboratory experiments by taking a sample at the wellhead or from the reservoir under downhole conditions. However, this process is time-consuming and very costly. To overcome these challenges, empirical correlations and artificial intelligence (AI) models can be applied to obtain these properties. The objective of this paper is to introduce new empirical correlations to estimate Pb and Rs based on three input parameters—reservoir temperature and oil and gas gravities. 760 data points were collected from different sources to build new AI models for Pb and Rs. The new empirical correlations were developed by integrating artificial neural network (ANN) with a modified self-adaptive differential evolution algorithm to introduce a hybrid self-adaptive artificial neural network (SaDE-ANN) model. The results obtained confirmed the accuracy of the developed SaDE-ANN models to predict the Pb and Rs of crude oils. This is the first technique that can be used to predict Rs and Pb based on three input parameters only. The developed empirical correlation for Pb predicts the Pb with a correlation coefficient (CC) of 0.99 and an average absolute percentage error (AAPE) of 6%. The same results were obtained for Rs, where the new empirical correlation predicts the Rs with a coefficient of determination (R2) of 0.99 and an AAPE of less than 6%. The developed technique will help reservoir and production engineers to better understand and manage reservoirs. No additional or special software is required to run the developed technique.

show abstract

Modeling and Optimizing Rate of Penetration Using Intelligent Systems in an Iranian Southern Oil Field (Ahwaz Oil Field)

Cited by 73 publications

References 2 publications

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

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

New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation

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

Contact Info

Product

Resources

About