Data-Driven PHM Solution for Health Monitoring of Mud Motor Power Sections While Drilling

2021 ◽  
Author(s):  
Dmitry Belov ◽  
Samba BA ◽  
Ji Tang Liu ◽  
Anton Kolyshkin ◽  
Sergio Daniel Rocchio
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Maintenance Cost ◽  
High Fidelity ◽  
Great Success ◽  
Motor Power ◽  
Gas Industry

Abstract Mud motors are widely used for directional and performance drilling. Due to the extremely challenging operating conditions, they are prone to failures, resulting in unnecessary maintenance repair costs as well as unpredictable and very costly drilling failure. Until now, the oil and gas industry has lacked reliable procedures to monitor and maintain the health of the mud motor power sections. Recently, we systematically addressed this problem with an industry unique prognostic health management solution, which not only tracks remaining useful life (RUL), but also creates a new failure prevention scheme for operators. The key objective of this solution is to reduce maintenance costs and improve mud motor fleet reliability. It's based on a high-fidelity model and uses a hybrid approach by combining a high-fidelity physics-based model of a power section and data-driven approaches with machine learning techniques for real-time applications. The new methodology was tested in the field with great success. The verification of the created solution was completed based on numerous field data from Saudi Arabia and Argentina. Comparison of the predicted mud motor fatigue values with the actual observed post-job conditions and job failures demonstrated high fidelity of the developed models. The whole solution is currently being integrated into a drilling platform including the maintenance system, the well construction planning, and the execution. The first application of the workflow was deployed in the field in Colombia targeting reduction of maintenance cost and failure avoidance. The result was outstanding, with the initial deployment bringing about 27% of projected yearly maintenance savings and 10% of projected yearly failure reduction. It enables using the equipment to the full extent with increased drilling performance without sacrificing reliability. In addition, it optimizes the entire fleet management with reduced cost of logistics and maintenance. The findings of this paper demonstrate the value of the mud motor PHM solution for the oil and gas industry by providing accurate prognosis of power section health, leading to reduced costs, minimized NPT, and increased operational reliability.

A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

2013 ◽  
Vol 135 (11) ◽  
Author(s):  
Rainer Kurz ◽  
J. Michael Thorp ◽  
Erik G. Zentmyer ◽  
Klaus Brun
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Process Conditions ◽  
Plant Design ◽  
Well Performance ◽  
Worst Case ◽  
Gas Industry ◽  
Machine Performance ◽  
Equipment Sizing

Equipment sizing decisions in the oil and gas industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions, and others. Since the ultimate goal is to meet production commitments, the traditional method of addressing this is to use worst case conditions and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances, by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, however, they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will, therefore, usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using a Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance. Case studies are presented that highlight the methodology as applied to critical turbomachinery.

Status of Data-Driven Methods and their Applications in Oil and Gas Industry

2018 ◽  
Author(s):  
Karthik Balaji ◽  
Minou Rabiei ◽  
Vural Suicmez ◽  
Celal Hakan Canbaz ◽  
Zinyat Agharzeyva ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Gas Industry

Wheel Box Test Aeromechanical Verification of New First Stage Bucket With Integrated Cover Plates for MS5002 GT

2019 ◽  
Author(s):  
Marco Mariottini ◽  
Nicola Pieroni ◽  
Pietro Bertini ◽  
Beniamino Pacifici ◽  
Alessandro Giorgetti
Keyword(s):  
Gas Turbine ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Response Assessment ◽  
Operating Conditions ◽  
Analytical Models ◽  
Gas Industry ◽  
Improved Performance ◽  
Cover Plates

Abstract In the oil and gas industry, manufacturers are continuously engaged in providing machines with improved performance, reliability and availability. First Stage Bucket is one of the most critical gas turbine components, bearing the brunt of very severe operating conditions in terms of high temperature and stresses; aeromechanic behavior is a key characteristic to be checked, to assure the absence of resonances that can lead to damage. Aim of this paper is to introduce a method for aeromechanical verification applied to the new First Stage Bucket for heavy duty MS5002 gas turbine with integrated cover plates. This target is achieved through a significantly cheaper and streamlined test (a rotating test bench facility, formally Wheel Box Test) in place of a full engine test. Scope of Wheel Box Test is the aeromechanical characterization for both Baseline and New bucket, in addition to the validation of the analytical models developed. Wheel Box Test is focused on the acquisition and visualization of dynamic data, simulating different forcing frequencies, and the measurement of natural frequencies, compared with the expected results. Moreover, a Finite Elements Model (FEM) tuning for frequency prediction is performed. Finally, the characterization of different types of dampers in terms of impact on frequencies and damping effect is carried out. Therefore, in line with response assessment and damping levels estimation, the most suitable damper is selected. The proposed approach could be extended for other machine models and for mechanical audits.

scholarly journals Stochastic Simplex Approximate Gradient for Robust Life-Cycle Production Optimization: Applied to Brugge Field

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Bailian Chen ◽  
Jianchun Xu
Keyword(s):  
Life Cycle ◽  
Net Present Value ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Optimization Techniques ◽  
Production Optimization ◽  
Operating Conditions ◽  
Optimization Under Uncertainty ◽  
Theoretical Argument ◽  
Gas Industry

In oil and gas industry, production optimization is a viable technique to maximize the recovery or the net present value (NPV). Robust optimization is one type of production optimization techniques where the geological uncertainty of reservoir is considered. When well operating conditions, e.g., well flow rates settings of inflow control valves and bottom-hole pressures, are the optimization variables, ensemble-based optimization (EnOpt) is the most popular ensemble-based algorithm for the robust life-cycle production optimization. Recently, a superior algorithm, stochastic simplex approximate gradient (StoSAG), was proposed. Fonseca and co-workers (2016, A Stochastic Simplex Approximate Gradient (StoSAG) for Optimization Under Uncertainty, Int. J. Numer. Methods Eng., 109(13), pp. 1756–1776) provided a theoretical argument on the superiority of StoSAG over EnOpt. However, it has not drawn significant attention in the reservoir optimization community. The purpose of this study is to provide a refined theoretical discussion on why StoSAG is generally superior to EnOpt and to provide a reasonable example (Brugge field) where StoSAG generates estimates of optimal well operating conditions that give a life-cycle NPV significantly higher than the NPV obtained from EnOpt.

scholarly journals Predicting the Dynamic Parameters of Multiphase Flow in CFD (Dam-Break Simulation) Using Artificial Intelligence-(Cascading Deployment)

Fluids ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 44 ◽  
Author(s):  
S. Hosseini Boosari
Keyword(s):  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Multiphase Flow ◽  
Fast Track ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Dam Break ◽  
Data Driven ◽  
Time Step ◽  
Gas Industry

Multiphase flow of oil, gas, and water occurs in a reservoir’s underground formation and also within the associated downstream pipeline and structures. Computer simulations of such phenomena are essential in order to achieve the behavior of parameters including but not limited to evolution of phase fractions, temperature, velocity, pressure, and flow regimes. However, within the oil and gas industry, due to the highly complex nature of such phenomena seen in unconventional assets, an accurate and fast calculation of the aforementioned parameters has not been successful using numerical simulation techniques, i.e., computational fluid dynamic (CFD). In this study, a fast-track data-driven method based on artificial intelligence (AI) is designed, applied, and investigated in one of the most well-known multiphase flow problems. This problem is a two-dimensional dam-break that consists of a rectangular tank with the fluid column at the left side of the tank behind the gate. Initially, the gate is opened, which leads to the collapse of the column of fluid and generates a complex flow structure, including water and captured bubbles. The necessary data were obtained from the experience and partially used in our fast-track data-driven model. We built our models using Levenberg Marquardt algorithm in a feed-forward back propagation technique. We combined our model with stochastic optimization in a way that it decreased the absolute error accumulated in following time-steps compared to numerical computation. First, we observed that our models predicted the dynamic behavior of multiphase flow at each time-step with higher speed, and hence lowered the run time when compared to the CFD numerical simulation. To be exact, the computations of our models were more than one hundred times faster than the CFD model, an order of 8 h to minutes using our models. Second, the accuracy of our predictions was within the limit of 10% in cascading condition compared to the numerical simulation. This was acceptable considering its application in underground formations with highly complex fluid flow phenomena. Our models help all engineering aspects of the oil and gas industry from drilling and well design to the future prediction of an efficient production.

Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing

2021 ◽  
Author(s):  
Rajeev Ranjan Sinha ◽  
Supriya Gupta ◽  
Praprut Songchitruksa ◽  
Saniya Karnik ◽  
Amey Ambade
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Learning Approaches ◽  
Submersible Pump ◽  
Gas Industry ◽  
Electrical Submersible Pump ◽  
Low Efficiency

Abstract Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety and relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs. The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis.

Risk-based Winterization for Vessels Operations in Arctic Environments

2013 ◽  
Vol 29 (04) ◽  
pp. 199-210 ◽  
Author(s):  
Ming Yang ◽  
Faisal I. Khan ◽  
Leonard Lye ◽  
Heri Sulistiyono ◽  
John Dolny ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Temperature Data ◽  
Operating Conditions ◽  
Hydrocarbon Exploration ◽  
The Arctic ◽  
Gas Industry ◽  
Arctic Regions ◽  
Exploration And Production

Because the oil and gas industry has an increasing interest in the hydrocarbon exploration and development in the Arctic regions, it becomes important to design exploration and production facilities that suit the cold and harsh operating conditions. In addition to well-established minimum class requirements for hull strengthening, winterization should be considered as a priority measure early in the design spiral for vessels operating in the Arctic environments. The development of winterization strategies is a challenging task, which requires a robust decision support approach. This article proposes a risk-based approach for the selection of winterization technologies and determination of winterization levels or requirements on a case-by-case basis. Temperature data are collected from climatology stations located in the Arctic regions. Loading scenarios are defined by statistical analysis of the temperature data to obtain probabilistic distributions for the loadings. Risk values are calculated under different loading scenarios. Based on the risk values, appropriate winterization strategies can be determined. A case study is used to demonstrate how the proposed approach can be applied to the identification of heating requirements for gangways.

A Workflow for Intelligent Data-driven Analytics Software Development in Oil and Gas Industry

2014 ◽  
Author(s):  
Serkan Dursun ◽  
Kaan Duman ◽  
Tayfun Tuna ◽  
Mamta Abbas ◽  
James Ding
Keyword(s):  
Software Development ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Gas Industry
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