Practical Application of Real-Time Expert System for Automatic Well Control

Lloyd, G.M.; Bode, Dennis; Nickens, Henry V.; Varnado, S.G.

doi:10.2118/19919-ms

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…Delta flow method is one of the simplest methods of detecting kick and loss, which was first introduced in and later discussed in Lloyd et al, 1990;Schafer et al, 1991;Haeusler, Makohl, and Harris, 1995). Delta flow method uses the difference between the inflow of drilling fluid into the wellbore and the return flow of drilling fluid from the wellbore to detect unusual conditions as shown in Figure 1.3b.…”

Section: Delta Flow Methodsmentioning

confidence: 99%

“…(Caenn et al, 2011) One way of maintaining the stability of bottom-hole pressure is by monitoring and regulating the drilling fluid flow rate. An early indication of wellbore instability can be detected using delta flow method, which is based on the difference between inflow and outflow measurements of drilling fluid while circulating the fluid, (Maus et al, 1979;Schafer et al, 1992;Lloyd et al, 1990). Therefore, it is important to measure inflow and outflow of drilling fluid accurately.…”

Section: Short Overviewmentioning

confidence: 99%

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Upstream Ultrasonic Level Based Soft Sensing of Volumetric Flow of Non-Newtonian Fluids in Open Venturi Channels

et al. 2018

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This thesis is submitted to University of SouthEastern Norway (USN) for the degree of Doctor of Philosophy to the Department of Electrical Engineering, Information Technology, and Cybernetics under the Faculty of Technology, Natural Sciences, and Maritime Sciences. The research work is funded by the Ministry of Education and Research of the Norwegian Government, for four years with 25% teaching duties and starting from September 2014. The work is mainly related to flow measurement in the return line of drilling fluid circulation while drilling. In any drilling operations, wellbore stability is the primary objective for safe and efficient drilling. The study focuses on the usage of the delta flow measurement (i.e., the difference between inflow and return flow) for maintaining the wellbore stability. An accurate return flow measurement is a comparatively challenging task, which is investigated in this study. For the return flow measurement, a simple and accurate flow measurement system using Venturi constriction is presented that may replace an existing uniform open channel. For the study, three different types of existing flow models are investigated. Different machine learning based flow models are developed. The models are tested in a flow loop available at USN, Campus Porsgrunn using synthetic drilling fluids with rheological properties that are comparable with water-based drilling mud. The experimental results show that the models are applicable for non-Newtonian fluid flow measurements. I hope the models will be of use in the real drilling operations for both inflow and outflow measurements. vii Acknowledgement I would like to express my sincere gratitude towards my supervisor Saba Mylvaganam for his help and support in this work. I would also like to thank my cosupervisor Håkon Viumdal for his valuable contribution. We three together as a team has successfully managed to complete this work in time. My sincere thanks also go to my co-supervisor Gerhard Nygaard for sharing his expert knowledge on drilling operations during the early period of the work. I am grateful to the University of SouthEastern Norway and the Ministry of Education and Research of the Norwegian Government for funding the work. I would like to thank Equinor ASA for providing and commissioning the flow loop with various types of sensors and control systems dedicated to flow studies. The economic support from the Research Council of Norway and Equinor ASA through project no. 255348/E30 "Sensors and models for improved kick/loss detection in drilling (Semi-kidd)" is gratefully acknowledged. I greatly appreciate and acknowledge the expert advice on drilling operations by Dr. Geir Elseth of Equinor. I thank my colleagues and friends; Rajan

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Section: Delta Flow Methodsmentioning

confidence: 99%

Section: Short Overviewmentioning

confidence: 99%

Upstream Ultrasonic Level Based Soft Sensing of Volumetric Flow of Non-Newtonian Fluids in Open Venturi Channels

et al. 2018

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“…After that choose the kill method and start performing killing procedures. [10][11][12][13][14][15][16][17][18][19]…”

Section: Causes Of Kickmentioning

confidence: 99%

Excel Sheet Program to Select Optimum Well Kill Method during Drilling

Sayed¹

2023

TPE

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Well control is an extremely critical operation that requires a lot of good planning and professional implementation. The selection of the proper kill method that is required to kill the well safely and efficiently either drilling or production well is a tedious decision. It requires a lot of accurate data from the current well conditions which will help the decision makers in their selections. Selecting the wrong kill method may end up with an unsafe and a high costly operation. This paper introduces an excel sheet program to select optimum well kill method that will help the engineering team as well as the operation team to take the proper decision regarding the optimum well control method to be applied. This paper provide a program built using simple excel sheet in which input data are well information. This information is analyzed and used to answer some questions. The answers for these questions have different weights. The system will select the highest score for these answers which will choose the optimum kill method based on the input data and the weighted value and provide kill sheet for the well according to the selected method. The program is tested in two different cases for drilling oil and gas wells. The system selected different kill methods based on each well criteria. The outputs were compared with a commercial simulator and the results are comparable which indicate that such a cheap excel program can be used easily and economically.

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A Decision Analytic Framework for Autonomous Geosteering

Rajaieyamchee

Bratvold

2010

SPE Annual Technical Conference and Exhibition

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One of the objectives of Integrated Operations (IO) 1 is to automate the decision-making process in drilling operations. Such automation is essential to improve both decision quality and speed in the complex and data-rich environment of drilling operations. Over the past decade, the E&P industry has significantly improved its ability to acquire and share large data volumes at unprecedented speeds. The motivation for these investments is to increase value through increased future productions while simultaneously reducing drilling costs. However, utilizing the large data volumes is not always straightforward. Sensor measurements are uncertain because of their reading errors and possible malfunctions, and many sources of data need to be fused to provide decision-relevant information. Furthermore, many drilling operational decisions must be made with very limited time, and this makes evaluating potential alternatives based on the decision criteria by the geosteering team difficult or even impossible. Given these challenges, there is a need to develop efficient processes and tools to automatically support drilling operational decisions. In this paper, we propose a modular intelligent decision advisor (IDA) framework for supporting geosteering decisions. The first module validates and combines the sensor data to determine the probability of the sensor faults and failures as well as undesired geosteering events. These events may include a dramatic increase in drag friction factors, approaching bed boundaries, tools damage, etc. Failure to detect these events could lead to losing the steering ability and sometimes catastrophic problems. The second module proactively analyzes geosteering hazards and recommends the best alternatives. To perform the sensor validation/fusion and hazard/decision analysis, we suggest the use of influence diagrams, also known as Bayesian decision networks (BDNs). Developing an IDA can lead to the reduction of human operators from rig-sites, drawing its value from improving the human safety and geosteering efficiency. Implementing IO with its associated technologies such as large volume data transmission and computational capabilities provides the means for this autonomous decision advisor. Our results show that the BDN is a useful and relevant element in the autonomous geosteering decision-making process. IntroductionHorizontal drilling is well established as a cost effective solution for exploiting oil and gas in challenging environments such as deep water and thin formations. To place the wellbore in an optimal trajectory, the directional driller adjusts the drill-bit position (inclination and azimuth) to reach one or more geological targets. These adjustments are based on the analysis and interpretation of real-time sensor readings and subsurface model outputs, combined with subject-experts' knowledge in the team. The success of geosteering operations, therefore, relies on timely access to relevant decision-supporting information. To optimize drilling operations, operators have gradually inc...

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Practical Application of Real-Time Expert System for Automatic Well Control

Cited by 11 publications

References 3 publications

Upstream Ultrasonic Level Based Soft Sensing of Volumetric Flow of Non-Newtonian Fluids in Open Venturi Channels

Upstream Ultrasonic Level Based Soft Sensing of Volumetric Flow of Non-Newtonian Fluids in Open Venturi Channels

Excel Sheet Program to Select Optimum Well Kill Method during Drilling

A Decision Analytic Framework for Autonomous Geosteering

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