Secure Drilling Services for Safe and Effective Drilling (Russian)

Kulikov, Stanislav; Veliev, Gurban; Bakhtin, Andrey; Zakomoldin, Vitaliy

doi:10.2118/166846-ru

Cited by 4 publications

(1 citation statement)

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“…In recent years, some domestic and foreign related lost circulation prediction and diagnosis technologies, such as fine pressure control drilling technology, comprehensive logging technology, separator liquid level detection method and downhole micro flow detection method [3], have made good development, which can be applied to intelligent early warning in oil drilling engineering. However, these methods for lost circulation mainly involve experts proposing solutions in combination with the site conditions of the lost circulation, relying too much on the experience of field experts to achieve pre-drill diagnosis and ignoring other lost circulation decision-making schemes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning

Wen¹,

Chen²,

Zhou³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Lost circulation during drilling wells is very detrimental since it greatly increases the non-productive time and operational cost, also seriously lead to wellbore instability, pipe sticking, blow out, etc.. However, in the process of drilling wells, geological characteristics and operational drilling parameters all may have impacts to the lost circulation. This makes the establishment of the relations between the lost circulation and drilling factors very challenging. In this paper, we tested five different kernel function (linear, quadratic, cubic, medium Gaussian and fine Gaussian) derived support vector regression (SVR) models and four-layer artificial neural network (ANN). By combining their accuracy and time efficiency, the ANN is regarded as the optimal predictor of lost circulation. By training ANN using different combination of drilling features, we concluded that depth, torque, hanging weight, displacement, entrance density and export density are the key factors to accurate predict the lost circulation. The corresponding trained ANN network can achieve 99.2% accuracy and evaluate whether a drilling feature vector corresponds to lost circulation or not in milliseconds.

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Section: Introductionmentioning

confidence: 99%

Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning

Wen¹,

Chen²,

Zhou³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

View full text Add to dashboard Cite

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Research on Drilling Overflow Warning Method Based on Logging Data and LSTM

Duan¹,

Wang²,

Yang³

et al. 2023

2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

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Drilling Efficiency and Geological Restrictions in Carbonate Type Collector with MPD in Compare with Conventional Methods

Kulikov

Dmitry

Bakhtin

et al. 2015

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Riphean anisotropic cavernous-fissured carbonate reservoirs of oilfield at East Siberia area – one of the most problematic objects of oil and gas production described in this article. Here is discussed geological substantiation and first result of new technology involving in drilling of Riphean carbonate reservoirs with "Controlled pressure technology" by recovery wells with horizontal bottom at the oil-gas condensate field in Evenki Region. Comparison results of conventional drilling and "MPD" (Managed Pressure Drilling) technology. Riphean reservoirs drilling process could be provided in two options. The first one – horizontal conventional drilling. As far as the bit penetrates into the reservoir we are seeing SPP (Stand Pipe Pressure) decrease which means we have total lost circulation. There was only one means for resolving this problem – using LCM (Lost Circulation Material). Due to complicated geology and high parameter of gas factor in Riphean reservoirs (losses while circulation/ gains while static), LCM was insufficient solution. The second is to perform drilling process using MPD (Managed Pressure Drilling) technology. We have the advantage using lower mud weight compared with conventional drilling. MPD technology corresponds with geological conditions of Riphean reservoirs and can be more profitable in future production from these wells. MPD technology with closed loop circulation system showed that we can "manage" BHP (Bottom Hole Pressure) and it really works in temperature conditions of 45 Celsius below zero in Evenki Region (East Siberia). In practice we have seen, that it was possible to achieve more gentle management of BHP than with conventional drilling using LCM. Also with "MPD" system in this oilfield we were able to drill the first ERD (Extended Reach Drilling) well. However, even with such extended horizontal section (1000 meters), a significant reduction of losses was achieved without using LCM. Total results of MPD technology we will see after DST (Drill Stem Test) of drilled wells. New technology showed that it was a right choice to use close loop system and MPD. Also for drilling new wells in this Riphean carbonate reservoirs of this oilfield area we will use this experience. Best way to reduce catastrophic losses in abnormally low pressure reservoir conditions is reduce and control the ECD and details how to achieve it we have considered in this article. The results: Reduced interval construction time. From 31 days to 24 days.Reduced fluid loss during drilling. From 2000 m3 to 1200 m3.Improved the operational safety by closing the loop and real time early kick and loss detection.

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Secure Drilling Services for Safe and Effective Drilling (Russian)

Cited by 4 publications

References 0 publications

Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning

Intelligent prediction and key factor analysis to lost circulation from drilling data based on machine learning

Research on Drilling Overflow Warning Method Based on Logging Data and LSTM

Drilling Efficiency and Geological Restrictions in Carbonate Type Collector with MPD in Compare with Conventional Methods

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