Roar Nybø - Academia.edu (original) (raw)

Papers by Roar Nybø

SPE Annual Technical Conference and Exhibition, 2015

SPE Intelligent Energy Conference & Exhibition, 2014

ABSTRACT 1. Description of the material: Development of real-time computer-assisted decision supp... more ABSTRACT 1. Description of the material: Development of real-time computer-assisted decision support tends to focus on the challenges of integrating data silos and integrating disparate computer systems. Often the integration and decision support is seen as a means towards a greater level of automation. However, these integration efforts, which are fundamentally integration between computers, may have negative effects on the integration of the human with the computers. This is a known dilemma in the automation industry and we identify it as one of the larger integration gaps facing intelligent energy. We will here focus on decision support during critical events in drilling and outline a roadmap for closing the integration gap. 2. Application: Application is in the concurrent design of work processes and supporting technology surrounding drilling operations, carried out by operators and service companies. 3. Results, Observations, and Conclusions: Following a review of recent trends in decision support systems, we identify limitations of current approaches and show how the now prevalent onshore collaboration centers allow the design of fundamentally new alarm and decision support systems. In particular, we identify day-to-day and real-time maintenance of computer models as a key task for supporting the team's shared understanding of the drilling operation. We also identify critical situations in which current decision support systems are performing poorly and suggest ways of improving this. 4. Significance of subject matter: The paper counters the more naïve arguments equating increased automation with increased efficiency and define design criteria for the smart decision support systems of the future.

2008 SPE Europec/EAGE Annual Conference and Exhibition, 2008

Intelligent Energy Conference and Exhibition, 2008

Offshore Europe 2009, 2009

Neurocomputing, 2010

Data-centric methods like soft computing and machine learning have gained greater interest and ac... more Data-centric methods like soft computing and machine learning have gained greater interest and acceptance in the oil and gas industry in recent years. We give an overview of the opportunities and challenges facing applied time series prediction in this domain, with a focus on fault prediction. In particular, we argue that the physical processes and hierarchies of information flow in the industry strongly determine the choice of soft computing or machine learning methods.

Automatic Control in Offshore Oil and Gas Production - Elsevier IFAC Publications / IFAC Proceedings series, 2012

ABSTRACT To ensure safe and stable drilling operation, bottomhole pressure(BHP) should be kept wi... more ABSTRACT To ensure safe and stable drilling operation, bottomhole pressure(BHP) should be kept within some region. However measurement of the BHP is sometimes not available or reliable, especially when the circulation is low, e.g., during pipe connection procedures. This paper presents the application of a moving horizon estimation (MHE) method for online estimation of the BHP during petroleum drilling. In the proposed MHE formulation the states are estimated by a forward simulation with a pre-estimating observer. Moreover, it considers the constraints of states/outputs in the MHE problem. Application of the observer to a real data set from a North Sea oil well illustrates potential benefits.

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013

ABSTRACT The increase of drilling safety and the reduction of drilling operation costs, especiall... more ABSTRACT The increase of drilling safety and the reduction of drilling operation costs, especially the improvement of drilling efficiency, are two important considerations. In general the rate of penetration (ROP) optimization means that the drilling parameters such as weight on bit (WOB) and rotary speed (RPM) are adjusted to drill the present formation most efficiently. In this paper, the Bourgoyne and Young ROP model had been selected to study the effects of several parameters during drilling operation. We present an advanced method for the ROP calculation and its optimization. A moving-horizon multiple regression method is proposed, which reduces the estimation error of the existing ROP models by continuously calibrating the model coefficients based on real-time data. Furthermore, a model predictive control (MPC) strategy is applied to achieve the ROP optimization to satisfy drilling requirements. The performance of the methodology is demonstrated by using realworld data from a North Sea well.

SPE Intelligent Energy International, 2012

SPE Intelligent Energy International, 2012

IEEE Transactions on Control Systems Technology, 2000

ABSTRACT Moving-horizon estimation provides a general method for state estimation with strong the... more ABSTRACT Moving-horizon estimation provides a general method for state estimation with strong theoretical convergence properties under the critical assumption that global solutions are found to the associated nonlinear programming problem at each sampling instant. A particular benefit of the approach is the use of a moving window of data that is used to update the estimate at each sampling instant. This provides robustness to temporary data deficiencies such as lack of excitation and measurement noise, and the inherent robustness can be further enhanced by introducing regularization mechanisms. In this paper, we study moving-horizon estimation in cases when output measurements are lost or delayed, which is a common situation when digitally coded data are received over low-quality communication channels or random access networks. Modifications to a basic moving-horizon state estimation algorithm and conditions for exponential convergence of the estimation errors are given, and the method is illustrated by using a simulation example and experimental data from an offshore oil drilling operation.

SPE Intelligent Energy International, 2012

Journal of Natural Gas Science and Engineering, 2011

Complex operations carried out in the oil and gas industry such as drilling require constant and ... more Complex operations carried out in the oil and gas industry such as drilling require constant and accurate real-time monitoring of the process. To this aim, a real-time model of the drilling operation is required. Such a model is used to estimate the state of the well when and where direct and reliable measurements are not available and it helps the driller gain an overview of the drilling process. Given the harsh operating environment, sensor reliability and sensor calibration are known problem areas, and bad data quality is a common problem, affecting the accuracy of the model. As a result, the driller may be misled about the downhole situation or receive conflicting claims about operating conditions. A way to reduce uncertainty and increase confidence is to aggregate the opinion of different experts. When the expert is a computer program, such aggregation is often referred to as an ensemble approach. The principle underlies techniques that have become popular in the oil industry in recent years, such as probabilistic forecasting and ensemble Kalman filters. In this paper, we discuss this trend and develop an ensemble system for predicting the bottom-hole pressure during a managed pressure drilling operation. The improved accuracy and robustness of the ensemble approach in situations with bad data quality is demonstrated.

SPE Intelligent Energy International, 2012

In oil and gas industries, drilling is a complex and critical operation which require constant an... more In oil and gas industries, drilling is a complex and critical operation which require constant and accurate real-time monitoring. To this aim, real-time models are required to provide an overview of the drilling operations when direct and reliable measurements are not available. Given the harsh operating environment, sensor reliability and calibration are critical issues and bad data quality is a typical problem which affects the accuracy of the model. As a result, the driller may be misled about the down-hole situation or receive conflicting claims about operating conditions. This paper presents two approaches based on the use of artificial intelligence to improve monitoring of drilling processes in terms of reduced uncertainty and increased confidence. The first exploits the aggregation of the opinion of different experts within a so-called ensemble approach; the second is based on a so-called grey-box approach which combines a physical model and artificial intelligence. The two approaches are applied to the problem of predicting the bottom-hole pressure during a managed pressure drilling operation to demonstrate the improved accuracy and robustness.

SPE Annual Technical Conference and Exhibition, 2015

SPE Intelligent Energy Conference & Exhibition, 2014

ABSTRACT 1. Description of the material: Development of real-time computer-assisted decision supp... more ABSTRACT 1. Description of the material: Development of real-time computer-assisted decision support tends to focus on the challenges of integrating data silos and integrating disparate computer systems. Often the integration and decision support is seen as a means towards a greater level of automation. However, these integration efforts, which are fundamentally integration between computers, may have negative effects on the integration of the human with the computers. This is a known dilemma in the automation industry and we identify it as one of the larger integration gaps facing intelligent energy. We will here focus on decision support during critical events in drilling and outline a roadmap for closing the integration gap. 2. Application: Application is in the concurrent design of work processes and supporting technology surrounding drilling operations, carried out by operators and service companies. 3. Results, Observations, and Conclusions: Following a review of recent trends in decision support systems, we identify limitations of current approaches and show how the now prevalent onshore collaboration centers allow the design of fundamentally new alarm and decision support systems. In particular, we identify day-to-day and real-time maintenance of computer models as a key task for supporting the team's shared understanding of the drilling operation. We also identify critical situations in which current decision support systems are performing poorly and suggest ways of improving this. 4. Significance of subject matter: The paper counters the more naïve arguments equating increased automation with increased efficiency and define design criteria for the smart decision support systems of the future.

2008 SPE Europec/EAGE Annual Conference and Exhibition, 2008

Intelligent Energy Conference and Exhibition, 2008

Offshore Europe 2009, 2009

Neurocomputing, 2010

Data-centric methods like soft computing and machine learning have gained greater interest and ac... more Data-centric methods like soft computing and machine learning have gained greater interest and acceptance in the oil and gas industry in recent years. We give an overview of the opportunities and challenges facing applied time series prediction in this domain, with a focus on fault prediction. In particular, we argue that the physical processes and hierarchies of information flow in the industry strongly determine the choice of soft computing or machine learning methods.

Automatic Control in Offshore Oil and Gas Production - Elsevier IFAC Publications / IFAC Proceedings series, 2012

ABSTRACT To ensure safe and stable drilling operation, bottomhole pressure(BHP) should be kept wi... more ABSTRACT To ensure safe and stable drilling operation, bottomhole pressure(BHP) should be kept within some region. However measurement of the BHP is sometimes not available or reliable, especially when the circulation is low, e.g., during pipe connection procedures. This paper presents the application of a moving horizon estimation (MHE) method for online estimation of the BHP during petroleum drilling. In the proposed MHE formulation the states are estimated by a forward simulation with a pre-estimating observer. Moreover, it considers the constraints of states/outputs in the MHE problem. Application of the observer to a real data set from a North Sea oil well illustrates potential benefits.

2013 10th IEEE International Conference on Control and Automation (ICCA), 2013

ABSTRACT The increase of drilling safety and the reduction of drilling operation costs, especiall... more ABSTRACT The increase of drilling safety and the reduction of drilling operation costs, especially the improvement of drilling efficiency, are two important considerations. In general the rate of penetration (ROP) optimization means that the drilling parameters such as weight on bit (WOB) and rotary speed (RPM) are adjusted to drill the present formation most efficiently. In this paper, the Bourgoyne and Young ROP model had been selected to study the effects of several parameters during drilling operation. We present an advanced method for the ROP calculation and its optimization. A moving-horizon multiple regression method is proposed, which reduces the estimation error of the existing ROP models by continuously calibrating the model coefficients based on real-time data. Furthermore, a model predictive control (MPC) strategy is applied to achieve the ROP optimization to satisfy drilling requirements. The performance of the methodology is demonstrated by using realworld data from a North Sea well.

SPE Intelligent Energy International, 2012

SPE Intelligent Energy International, 2012

IEEE Transactions on Control Systems Technology, 2000

ABSTRACT Moving-horizon estimation provides a general method for state estimation with strong the... more ABSTRACT Moving-horizon estimation provides a general method for state estimation with strong theoretical convergence properties under the critical assumption that global solutions are found to the associated nonlinear programming problem at each sampling instant. A particular benefit of the approach is the use of a moving window of data that is used to update the estimate at each sampling instant. This provides robustness to temporary data deficiencies such as lack of excitation and measurement noise, and the inherent robustness can be further enhanced by introducing regularization mechanisms. In this paper, we study moving-horizon estimation in cases when output measurements are lost or delayed, which is a common situation when digitally coded data are received over low-quality communication channels or random access networks. Modifications to a basic moving-horizon state estimation algorithm and conditions for exponential convergence of the estimation errors are given, and the method is illustrated by using a simulation example and experimental data from an offshore oil drilling operation.

SPE Intelligent Energy International, 2012

Journal of Natural Gas Science and Engineering, 2011

Complex operations carried out in the oil and gas industry such as drilling require constant and ... more Complex operations carried out in the oil and gas industry such as drilling require constant and accurate real-time monitoring of the process. To this aim, a real-time model of the drilling operation is required. Such a model is used to estimate the state of the well when and where direct and reliable measurements are not available and it helps the driller gain an overview of the drilling process. Given the harsh operating environment, sensor reliability and sensor calibration are known problem areas, and bad data quality is a common problem, affecting the accuracy of the model. As a result, the driller may be misled about the downhole situation or receive conflicting claims about operating conditions. A way to reduce uncertainty and increase confidence is to aggregate the opinion of different experts. When the expert is a computer program, such aggregation is often referred to as an ensemble approach. The principle underlies techniques that have become popular in the oil industry in recent years, such as probabilistic forecasting and ensemble Kalman filters. In this paper, we discuss this trend and develop an ensemble system for predicting the bottom-hole pressure during a managed pressure drilling operation. The improved accuracy and robustness of the ensemble approach in situations with bad data quality is demonstrated.

SPE Intelligent Energy International, 2012

In oil and gas industries, drilling is a complex and critical operation which require constant an... more In oil and gas industries, drilling is a complex and critical operation which require constant and accurate real-time monitoring. To this aim, real-time models are required to provide an overview of the drilling operations when direct and reliable measurements are not available. Given the harsh operating environment, sensor reliability and calibration are critical issues and bad data quality is a typical problem which affects the accuracy of the model. As a result, the driller may be misled about the down-hole situation or receive conflicting claims about operating conditions. This paper presents two approaches based on the use of artificial intelligence to improve monitoring of drilling processes in terms of reduced uncertainty and increased confidence. The first exploits the aggregation of the opinion of different experts within a so-called ensemble approach; the second is based on a so-called grey-box approach which combines a physical model and artificial intelligence. The two approaches are applied to the problem of predicting the bottom-hole pressure during a managed pressure drilling operation to demonstrate the improved accuracy and robustness.