Fractures and Flow Patterns Detection in Carbonate Reservoirs Using Intelligent Sensor Selection in a Deep Learning and Uncertainty Framework

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Abdallah Al Shehri ◽  
Ali Yousif
Keyword(s):  
Deep Learning ◽  
Industrial Revolution ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Sensor Selection ◽  
Sensor Data ◽  
Data Uncertainty ◽  
Fractured Reservoir ◽  
Sweep Efficiency ◽  
Reservoir Evaluation

Abstract 4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.

Deep learning health state prognostics of physical assets in the Oil and Gas industry

2020 ◽  
pp. 1748006X2097681
Author(s):  
Joaquín Figueroa Barraza ◽  
Luis Guarda Bräuning ◽  
Ruben Benites Perez ◽  
Carlos Bittencourt Morais ◽  
Marcelo Ramos Martins ◽  
...  
Keyword(s):  
Deep Learning ◽  
Oil And Gas ◽  
Treatment Plant ◽  
Oil And Gas Industry ◽  
Sensor Data ◽  
Health State ◽  
Gas Industry ◽  
Gas Treatment ◽  
Injection Pump ◽  
Physical Assets

Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this context, maintenance policies play a crucial role in the avoidance of unplanned downtimes and enhancement of productivity. In particular, Condition-Based Maintenance is an approach in which maintenance actions are performed depending on the assets’ health state that is evaluated through different kinds of sensors. In this paper, Deep Learning methods are explored and different models are proposed for health state prognostics of physical assets in two real-life cases from the Oil and Gas industry: a Natural Gas treatment plant in an offshore production platform where elevated levels of CO2 must be predicted, and a sea water injection pump for oil extraction stimulation, in which several degradation levels must be predicted. A general methodology for preprocessing the available multi-sensor data and developing proper models is proposed and apply in both case studies. In the first one, a LSTM autoencoder is developed, achieving precision values over 83.5% when predicting anomalous states up to 8 h ahead. In the second case study, a CNN-LSTM model is proposed for the pump’s health state prognostics 48 h ahead, achieving precision values above 99% for all possible pump health states.

A Comprehensive Review of the Fourth Industrial Revolution IR 4.0 in Oil and Gas Industry

2021 ◽  
Author(s):  
Cenk Temizel ◽  
Celal Hakan Canbaz ◽  
Hakki Aydin ◽  
Bahar F. Hosgor ◽  
Deniz Yagmur Kayhan ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Oil And Gas ◽  
Petroleum Industry ◽  
Oil And Gas Industry ◽  
Learning Technologies ◽  
Real Field ◽  
Gas Industry ◽  
History Of ◽  
History Of Use

Abstract Digital transformation is one of the most discussed themes across the globe. The disruptive potential arising from the joint deployment of IoT, robotics, AI and other advanced technologies is projected to be over $300 trillion over the next decade. With the advances and implementation of these technologies, they have become more widely-used in all aspects of oil and gas industry in several processes. Yet, as it is a relatively new area in petroleum industry with promising features, the industry overall is still trying to adapt to IR 4.0. This paper examines the value that Industry 4.0 brings to the oil and gas upstream industry. It delineates key Industry 4.0 solutions and analyzes their impact within this segment. A comprehensive literature review has been carried out to investigate the IR 4.0 concept's development from the beginning, the technologies it utilizes, types of technologies transferred from other industries with a longer history of use, robustness and applicability of these methods in oil and gas industry under current conditions and the incremental benefits they provide depending on the type of the field are addressed. Real field applications are illustrated with applications indifferent parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of machine learning technologies.

Clustering, Connectivity and Flow in Naturally Fractured Reservoir Analogs

2021 ◽  
Author(s):  
Ajay K. Sahu ◽  
Ankur Roy
Keyword(s):  
Fractal Dimension ◽  
Oil And Gas ◽  
Flow Behavior ◽  
Oil And Gas Industry ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Fracture Networks ◽  
Gas Industry ◽  
Network Geometry

Abstract A previous study by the authors on synthetic fractal-fracture networks showed that lacunarity, a parameter that quantifies scale-dependent clustering in patterns, can be used as a proxy for connectivity and also, is an indicator of fluid flow in such model networks. In this research, we apply the concepts thus developed to the study of fractured reservoir analogs and seek solutions to more practical problems faced by modelers in the oil and gas industry. A set of seven nested fracture networks from the Devonian Sandstone of Hornelen Basin, Norway that have the same fractal-dimension but are mapped at different scales and resolutions is considered. We compare these seven natural fracture maps in terms of their lacunarity and connectivity values to test whether the former is a reasonable indicator of the latter. Additionally, these maps are also flow simulated by implementing a fracture continuum model and using a streamline simulator, TRACE3D. The values of lacunarity, connectivity and fluid recovery thus obtained are pairwise correlated with one another to look for possible relationships. The results indicate that while fracture maps that have the same fractal dimension show almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering values change systematically with the scale at which the fracture networks are mapped. It is further noted that there appears to be a very good correlation between clustering, connectivity, and fluid recovery values for these fracture networks that belong to the same fractal system. The overall results indicate that while the fractal dimension is an important parameter for characterizing a specific type of fracture network geometry, it is the lacunarity or scale-dependent clustering attribute that controls connectivity in fracture maps and hence the flow properties. This research may prove helpful in quickly evaluating connectivity of fracture networks based on the lacunarity parameter. This parameter can therefore, be used for calibrating Discrete Fracture Network (DFN) models with respect to connectivity of reservoir analogs and can possibly replace the fractal dimension which is more commonly used in software that model DFNs. Additionally, while lacunarity has been mostly used for understanding network geometry in terms of clustering, we, for the first time, show how this may be directly used for understanding the potential flow behavior of fracture networks.

Characteristics of Fracture and Fractured Reservoir of Hailaer Basin Budart Group

2013 ◽  
Vol 316-317 ◽  
pp. 826-829
Author(s):  
Cui Ju Feng ◽  
Wei Lin Yan
Keyword(s):  
Oil And Gas ◽  
Low Permeability ◽  
Fractured Reservoir ◽  
Fracture Density ◽  
High Angle ◽  
Pore System ◽  
Reservoir Evaluation ◽  
Angle Fracture ◽  
Conventional Logs ◽  
Huge Challenge

The output of fracture pool is over half of the entire outout of oil and gas,and fracture pool is one of the important fields of oil inhancing yield in 21st Century. Fractured reservoir evaluation is always a huge challenge for the oil exploration and development. Budart Group of Sudert district in Hailaer Basin is a reservoir that has very low porosity、very low permeability、double pore system and it is rich of fracture. The paper summarized Hailaer Basin Budart Group reservoir’s characteristics, especially fractures’s characteristics in conventional logs,fracture’s parameters,such as fracture density,dip,width and filling and illustrate the response of low angle fracture and high angle fracture in logs.

Scale discrepancy paradox between observation and modelling in fractured reservoir models in oil and gas industry.

2020 ◽  
Author(s):  
Pascal Richard ◽  
Loïc Bazalgette
Keyword(s):  
Oil And Gas ◽  
Dynamic Models ◽  
Fractured Reservoirs ◽  
Research Work ◽  
Oil And Gas Industry ◽  
Naturally Fractured Reservoirs ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Dynamic Data ◽  
Gas Industry

<p>Naturally fractured reservoirs represent one of the most challenging resource in the oil and gas industry. The understanding based on centimeter scale observations is upscaled and modeled at 100-meter scale.</p><p>In this paper, we will illustrate with case study examples of conceptual fracture model elaborated using static and dynamic data, the disconnect between the scale of observation and the scale of modelling. We will also discuss the potential disconnect between the detail of fundamental, but necessary, research work in universities against the coarse resolution of the models built in the oil industry, and how we can benefit of the differences in scales and approaches.</p><p> </p><p>The appraisal and development of fractured reservoirs offer challenges due to the variations in reservoir quality and natural fracture distribution. Typically, the presence of open, connected fractures is one of the key elements to achieve a successful development. Fracture modelling studies are carried out routinely to support both appraisal and development strategies of these fractured reservoirs.</p><p>Overall fracture modelling workflow consists first of a fracture characterization phase concentrating on the understanding of the deformation history and the evaluation of the nature, type and distribution of the fractures; secondly of a fracture modelling part where fracture properties for the dynamic simulation are generated and calibrated against dynamic data. The pillar of the studies is the creation of 3D conceptual fracture diagrams/concepts which summarize both the understanding and the uncertainty of the fracture network of interest. These conceptual diagrams rely on detailed observations at the scale of the wellbore using core and borehole image data which are on contrasting scale compare to the 10’s of meters to 100’s of meter scale of the grid cells of the dynamic models used for the production history match and forecast. These contrasting scales will be the thread of the presentation.</p>

Fundamentals of Wireless Sensor Networks Using Machine Learning Approaches

2020 ◽  
pp. 233-254
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Learning Approaches ◽  
Gas Industry

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.

Accelerating Digital Transformation in E&P Business

2021 ◽  
Author(s):  
Anak Karim
Keyword(s):  
Industrial Revolution ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Digital Transformation ◽  
Gas Industry ◽  
New Normal ◽  
Strategic Objectives ◽  
The Future ◽  
Energy Companies

Abstract As a resourced based economy, Malaysia relies heavily on the energy oil, and gas industry - a critical sector contributing to the economic growth of the Malaysian economy; which makes up in the range of 20% - 25% of the total gross domestic product (GDP) of Malaysia as of 2017. No analysts can properly predict prices of the future, with the highs and lows of crude and natural gas and renewables as the fuel of the future and are perhaps new way of things. This "new normal" in which countries, including Malaysia, must learn to adapt in a more agile manner to the "new way of work" of increased productivity and efficiency (de Graauw, McCreery, & Murphy, 2015). In adapting to the new normal, measures of increased productivity must continue to be pushed forward and implemented. Energy companies and services provider still need to continue with exploration and development (E&P) operations and activities to meet long term strategic objectives and demands of the nation, in line with the aspirations of the national oil company, however, it needs to add more value to every dollar spent as margins have continued to shrink and reduce profit margins of energy producers. This is where Digital Transformation comes into play and the urgency for implementation has gone from novelty solutions to critical business survival. Changing industry trends such as Industrial Revolution 4.0 have made it more prevalent than ever to make better use of capital at a time when productivity is essential. At the same time, the industry needs to continue to explore and develop to meet long-term demands, which continues to grow albeit slower than before.

Scale discrepancy paradox between observation and modelling in fractured reservoir models in oil and gas industry.

2021 ◽  
Author(s):  
Pascal Richard ◽  
Loic Bazalgette
Keyword(s):  
Oil And Gas ◽  
Dynamic Models ◽  
Fractured Reservoirs ◽  
Research Work ◽  
Oil And Gas Industry ◽  
Naturally Fractured Reservoirs ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Dynamic Data ◽  
Gas Industry

<p>Naturally fractured reservoirs represent one of the most challenging resource in the oil and gas industry. The understanding based on centimeter scale observations is upscaled and modeled at 100-meter scale.</p><p>In this paper, we will illustrate with case study examples of conceptual fracture model elaborated using static and dynamic data, the disconnect between the scale of observation and the scale of modelling. We will also discuss the potential disconnect between the detail of fundamental, but necessary, research work in universities against the coarse resolution of the models built in the oil industry, and how we can benefit of the differences in scales and approaches.</p><p> </p><p>The appraisal and development of fractured reservoirs offer challenges due to the variations in reservoir quality and natural fracture distribution. Typically, the presence of open, connected fractures is one of the key elements to achieve a successful development. Fracture modelling studies are carried out routinely to support both appraisal and development strategies of these fractured reservoirs.</p><p>Overall fracture modelling workflow consists first of a fracture characterization phase concentrating on the understanding of the deformation history and the evaluation of the nature, type and distribution of the fractures; secondly of a fracture modelling part where fracture properties for the dynamic simulation are generated and calibrated against dynamic data. The pillar of the studies is the creation of 3D conceptual fracture diagrams/concepts which summarize both the understanding and the uncertainty of the fracture network of interest. These conceptual diagrams rely on detailed observations at the scale of the wellbore using core and borehole image data which are on contrasting scale compare to the 10’s of meters to 100’s of meter scale of the grid cells of the dynamic models used for the production history match and forecast. These contrasting scales will be the thread of the presentation.</p>

scholarly journals Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction

2021 ◽  
Vol 13 (6) ◽  
pp. 13-24
Author(s):  
Khalid Salmanov ◽  
◽  
Hadi Harb
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Maintenance Cost ◽  
System Failure ◽  
Gas Industry ◽  
Reduction Techniques

Middle size gas/diesel aero-derivative power generation engines are widely used on various industrial plants in the oil and gas industry. Bleed of Valve (BOV) system failure is one of the failure mechanisms of these engines. The BOV is part of the critical anti-surge system and this kind of failure is almost impossible to identify while the engine is in operation. If the engine operates with BOV system impaired, this leads to the high maintenance cost during overhaul, increased emission rate, fuel consumption and loss in the efficiency. This paper proposes the use of readily available sensor data in a Supervisory Control and Data Acquisition (SCADA) system in combination with a machine learning algorithm for early identification of BOV system failure. Different machine learning algorithms and dimensionality reduction techniques are evaluated on real world engine data. The experimental results show that Bleed of Valve systems failures could be effectively predicted from readily available sensor data.

scholarly journals Cybersecurity in complex operations: a post-drilling approach foroil and gas wells

2021 ◽  
Vol 266 ◽  
pp. 09007
Author(s):  
R.A. Perdomo ◽  
N.I. Serdyuk
Keyword(s):  
Performance Indicators ◽  
Business Models ◽  
Industrial Revolution ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Industrial Processes ◽  
Gas Industry ◽  
Fourth Industrial Revolution ◽  
Drilling Operations ◽  
And Performance

Many companies are now workingto converge business models and engineering processes with the technologies of the fourth industrial revolution. The automation of industrial processes together with the implementationof infrastructure,makes it possible to interconnect people and performance indicators in real-time, reducing the decision-making time and time in themarketplace. This provides companies with unprecedented opportunities to create and capture value while rethinking business models but brings vulnerabilities and risks that must be properly assessed and mitigated. New malicious third parties are emerging and directly threaten the efforts of companies. The purpose of this article is to enumerate the possible attackers (vectors) and to define the possible areas where they can attack companies (surfaces), illustratingwith the example of drilling operations in the oil and gas industry, in order to provide discussion points about what new competencies technicians need to developto face these emerging threats.

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