Application of Artificial Intelligent Systems in ROP Optimization: A Case Study in Shadegan Oil Field

Bataee, Mahmood; Mohseni, S.

doi:10.2118/140029-ms

Cited by 33 publications

(9 citation statements)

References 8 publications

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“…Bataee [20] also used ANN to predict ROP and improve drilling parameters. He used a larger dataset, 1810 data records, of five input parameters including RPM, WOB, MW, depth, and bit diameter.…”

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

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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.

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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

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“…Artificial Neural Network are powerful techniques used in modeling complex systems that seeks to simulate human brain behavior by treatment of data on the basis of trial and error. ANN has been identified as tool to determine and optimize complicated nonlinear relationships between parameters [6].…”

Section: -Artificial Neural Network Approachmentioning

confidence: 99%

“…M.Bataee et al (2011) developed an ANN model to determine complex relationship between drilling variables. Their model predicted the exact penetration rate, optimization of drilling parameters, time of the drilling of wells, and lowering the drilling cost for future wells [6].…”

Section: -Introductionmentioning

confidence: 99%

Prediction of penetration Rate and cost with Artificial Neural Network for Alhafaya Oil Field

Mnati¹,

Hadi²

2018

IJCPE

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Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered from five drilled wells were involved in modeling process.Approximatlly,85 % of these data were used for training the ANN models, and 15% to assess their accuracy and direction of stability. The results of the simulation showed good matching between the raw data and the predicted values of ROP by Artificial Neural Network (ANN) model. In addition, a good fitness was obtained in the estimation of drilling cost from ANN method when compared to the raw data.

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“…Besides the existing models, various laboratory (Babatunde et al, 2011;Hoover & Middleton, 1981) and field studies (Bataee et al, 2010;Shirkavand & Hareland, 2009) have been conducted to determine the effect of parameters on ROP. In addition to the use of mathematical models, in some studies neural networks were suggested due to their ability to solve nonlinear problems in the ROP modeling (Bataee & Mohseni, 2011;Esmaeili et al, 2012). Due to lack of access to database in these studies, often less attention have been paid to the rock properties.…”

Section: Introductionmentioning

confidence: 99%

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

Bezminabadi

Ramezanzadeh

Jalali

et al. 2017

Archives of Mining Sciences

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Rate of penetration (ROP) is one of the key indicators of drilling operation performance. The estimation of ROP in drilling engineering is very important in terms of more accurate assessment of drilling time which affects operation costs. Hence, estimation of a ROP model using operational and environmental parameters is crucial. For this purpose, firstly physical and mechanical properties of rock were derived from well logs. Correlation between the pair data were determined to find influential parameters on ROP. A new ROP model has been developed in one of the Azadegan oil field wells in southwest of Iran. The model has been simulated using Multiple Nonlinear Regression (MNR) and Artificial Neural Network (ANN). By adding the rock properties, the estimation of the models were precisely improved. The results of simulation using MNR and ANN methods showed correlation coefficients of 0.62 and 0.87, respectively. It was concluded that the performance of ANN model in ROP prediction is fairly better than MNR method. Keywords: ROP, rock properties, MNR, ANNPrędkość wiercenia jest jednym z podstawowych parametrów charakteryzujących tempo prac wiertniczych. Oszacowanie prędkości wiercenia jest zagadnieniem kluczowym dla inżynierów wiertnictwa, gdyż pozwala na dokładne określenie czasu trwania prac, a co za tym idzie także kosztów operacyjnych. Szacowanie prędkości wiercenia odbywa się na podstawie modelu uwzględniającego parametry pracy oraz parametry środowiskowe. Pierwszy krok obejmuje pozyskanie danych o fizycznych i mechanicznych właściwościach skał na podstawie profilowania geofizycznego otworu. Zastosowano korelację odpowiednich par danych dla pokreślenie wpływu głównych czynników warunkujących prędkość wiercenia. Nowy model obliczania prędkości wiercenia opracowany został w okręgu naftowym Azadegan w południo -wo-zachodniej części Iranu. Symulacje prowadzono w oparciu o metodę wielokrotnej regresji nieliniowej a także przy wykorzystaniu sztucznych sieci neuronowych. Poprzez dodanie danych o właściwościach

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Application of Artificial Intelligent Systems in ROP Optimization: A Case Study in Shadegan Oil Field

Cited by 33 publications

References 8 publications

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

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

Prediction of penetration Rate and cost with Artificial Neural Network for Alhafaya Oil Field

Effect of Rock Properties on ROP Modeling Using Statistical and Intelligent Methods: A Case Study of an Oil Well in Southwest of Iran

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