Development of Artificial Neural Networks to Predict Differential Pipe
Sticking in Iranian Offshore Oil Fields

Miri, Rahman; Sampaio, J. M.; Afshar, Mahmoud; Lourenco, Affonso

doi:10.2523/108500-ms

Cited by 11 publications

(3 citation statements)

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“…The system can automatically decide and set each drawing parameter according to the designated control attributes and actual drilling data, for example, whether showing Y axis, or requiring horizontal scroll bars, graphics positioning, graphic gap, a group of graphic gaps, etc. In addition, although the browsers (IE, Fire Fox) to HTML resolution in different mode, namely international standards of W3C different applications, the control can automatically judge and process data according to the client's different browser [4]. Fig.…”

Section: B Multi-scheme Comparison and Statistical Analysismentioning

confidence: 98%

Applied Research of Scheme Optimization in the Drilling Support System

Cui

Ying-hu²

2010

2010 Third International Symposium on Information Processing

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In view of current characteristics of well drilling support system, analyzing mass of well history data, logging data and drilling materials of each oil field in China, a new scheme optimization method is established using comprehensive comparison and analysis of multi-well data. The new method is successfully applied to a distributed drilling support system that uses network and computer as operation platform, Microsoft ASP.Net as development platform based on Framework2.0. The principle and procedure of the method are elaborated, including multi-scheme statistics and interactive graphic comparison of drilling data, and multi-well data mining with corresponding mathematical algorithm. The system can provide drilling design scheme and graphic drilling data for drilling technology support, realize remote access for engineers in different areas, and achieve better well site application finally.

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Section: B Multi-scheme Comparison and Statistical Analysismentioning

confidence: 98%

Applied Research of Scheme Optimization in the Drilling Support System

Cui

Ying-hu²

2010

2010 Third International Symposium on Information Processing

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show abstract

“…The resulting techniques are being successfully applied in a variety of everyday technical, business, industrial, and medical applications (Sivanandam et al, 2006). Some applications in the O&G industry includes an application to predict drill-bit life based on tooth and bearing failure (Bilgesu et al, 1998), an application for drill-bit selection of drilling bits (Bilgesu et al, 2000), an application for estimating initial water saturation Goda et al, 2005, an application to predict differential pipe sticking problems (Miri et al, 2007), and an application to estimate formation permeability (Mohaghegh et al, 1994).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases

Kamyab

Sampaio

Qanbari

et al. 2010

Journal of Petroleum Science and Engineering

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“…negoita et al (2004) focused on the robust tuning of a "PID controller" using an evolutionary algorithm and fuzzy neural networks. An 85:15 stratification strategy was used by Miri, Sampaio, Afshar, and lourenco (2007) and Inoue (2009) in their applications with Ann models in the prediction of differential pipe sticking in Iranian offshore oil fields and the respondents' responses in a social science survey, respectively. Both of these groups neither gave any reason for their choice of these stratification strategies nor cited any reference to support the superiority of their stratification choices.…”

Section: Conventional Stratification Strategiesmentioning

confidence: 99%

Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study

Anifowose

Khoukhi

Abdulraheem

2016

Journal of Experimental & Theoretical Artificial Intelligen

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a research institute, center for petroleum and minerals, King fahd university of petroleum and minerals, Dhahran, Saudi arabia; b Department of Systems Engineering, King fahd university of petroleum and minerals, Dhahran, Saudi arabia; c Department of petroleum Engineering, King fahd university of petroleum and minerals, Dhahran, Saudi arabia ABSTRACT Cross-validation of soft computing techniques needs to be done efficiently to avoid overfitting and underfitting. This is more important in petroleum reservoir characterisation applications where the often-limited training and testing data subsets represent Wells with known and unknown target properties, respectively. Existing data stratification strategies have been haphazardly chosen without any experimental basis. In this study, the optimal training-testing stratification proportions have been rigorously investigated using the prediction of porosity and permeability of petroleum reservoirs as an experimental case. The comparative performances of seven traditional and advanced machine learning techniques were considered. The overall results suggested a recommendable optimum training stratification that could serve as a good reference for researchers in similar applications. 1 now with the Saudi arabian oil company, Exploration and petroleum Engineering center, advanced research center, Dhahran, Saudi arabia. 2 now with the mechanical Engineering Department, new York city college of technology, new York, nY, uSa.

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Development of Artificial Neural Networks to Predict Differential Pipe Sticking in Iranian Offshore Oil Fields

Cited by 11 publications

References 0 publications

Applied Research of Scheme Optimization in the Drilling Support System

Applied Research of Scheme Optimization in the Drilling Support System

Using artificial neural networks to estimate the z-factor for natural hydrocarbon gases

Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study

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