scholarly journals A comprehensive review of sucker rod pumps’ components, diagnostics, mathematical models, and common failures and mitigations

2021 ◽  
Author(s):  
Sherif Fakher ◽  
Abdelaziz Khlaifat ◽  
M. Enamul Hossain ◽  
Hashim Nameer
Keyword(s):  
Mathematical Models ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Holistic View ◽  
Comprehensive Review ◽  
Artificial Lift ◽  
Sucker Rod ◽  
Sucker Rod Pump

AbstractIn many oil reservoirs worldwide, the downhole pressure does not have the ability to lift the produced fluids to the surface. In order to produce these fluids, pumps are used to artificially lift the fluids; this method is referred to as artificial lift. More than seventy percent of all currently producing oil wells are being produced by artificial lift methods. One of the most applied artificial lift methods is sucker rod pump. Sucker rod pumps are considered a well-established technology in the oil and gas industry and thus are easy to apply, very common worldwide, and low in capital and operational costs. Many advancements in technology have been applied to improve sucker rod pumps performance, applicability range, and diagnostics. With these advancements, it is important to be able to constantly provide an updated review and guide to the utilization of the sucker rod pumps. This research provides an updated comprehensive review of sucker rod pumps components, diagnostics methods, mathematical models, and common failures experienced in the field and how to prevent and mitigate these failures. Based on the review conducted, a new classification of all the methods that can fall under the sucker rod pump technology based on newly introduced sucker rod pump methods in the industry has been introduced. Several field cases studies from wells worldwide are also discussed in this research to highlight some of the main features of sucker rod pumps. Finally, the advantages and limitations of sucker rod pumps are mentioned based on the updated review. The findings of this study can help increase the understanding of the different sucker rod pumps and provide a holistic view of the beam rod pump and its properties and modeling.

Value Oriented Engineering Solutions for Business Continuity

2021 ◽  
Author(s):  
Jose Luis Delgado Rivera
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Electrical Equipment ◽  
Business Continuity ◽  
Gas Industry ◽  
Holistic View ◽  
Emergency Situations ◽  
Business Operations ◽  
Safety And Reliability ◽  
Major Disruption

Abstract This paper presents a systemic approach using Engineering and analytics methods to avail the fastest and safest responses to recovering business operations after Abqaiq Plants major disruption after the 2019 September 14th incident. This new approach using value and agile engineering, risk management methodologies combined with the business continuity model suggested was successfully applied to recover Abqaiq Plants Operations after catastrophic events occurred. This paper pretends to serve as example about how the business continuity plan should response to a major emergency and how this planning activity could be effectively supported using a Value Oriented Engineering Solutions (VOES). This VOES approach is based on Business continuity framework and adapted for use during emergency situations to generate effective and urgent responses to recover one of the most strategical operations in the Oil and Gas Industry worldwide ahead of the last year significant disruption. VOES approach vastly implemented during Abqaiq Plants Restoration allowed a 100% functional recovery on 9 days, 5 days in advance to the most optimistic scenario. This paper shows a case study implemented for major instrumentation and electrical equipment activities performed in UA Spheroids plant, one of the most affected area and responsible to process the 100% of the Abqaiq Plants Oil Production rate. This paper pretends to contribute with the research and practice on business continuity management. Considering a particular approach to BCM, incorporating value-oriented engineering solutions in the developing of continuity plans; we apply model-based techniques to provide quality assurance in the elaboration process, and to automate the generation/update of a BCP. On the practical side, this study converts Operational, Maintenance, Safety and Reliability perspectives in a holistic view provided from Engineering solutions responsible to generate the guidelines for an agile, effective and realizable recovery plan.

scholarly journals Factors Influencing the Enhancement of Energy Efficiency in the Oil and Gas Industry

2019 ◽  
Vol 290 ◽  
pp. 10007
Author(s):  
Silvian Suditu ◽  
Monica Emanuela Stoica ◽  
Tudora Cristescu
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Energy Resources ◽  
Energetic Efficiency ◽  
Main Concern ◽  
Gas Industry ◽  
Combined Cycles ◽  
Energy Strategies ◽  
Direct Applicability

In Romania, major focus and interest are currently expressed for the energy strategies domain. The increase of the energetic efficiency is therefore a main concern for the authorities. In the current paper the main factors which are leading to the increase of this energetic efficiency are highlighted. These are: CHP (Combined Heat Power), combined cycles, the use of RER (reusable energy resources). The article also contains a classification of the RES (secondary energy resources) as function of the thermodynamic agent nature and/or its thermodynamic state. The theory and concepts are strengthened with relevant examples which have direct applicability in the oil and gas industry.

scholarly journals Classification of oil and gas-bearing territories of Russia and the structure of the oil and gas industry of the regions

2021 ◽  
Vol 7 (2) ◽  
pp. 422-427
Author(s):  
Danil Petrovitch Egorov
Keyword(s):  
Russian Federation ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
The State ◽  
Qualitative Assessment ◽  
Resource Base ◽  
Gas Industry ◽  
The Russian Federation ◽  
Gas Bearing

The article is devoted to the qualitative assessment of the state of the oil and gas industry in the Russian Federation based on the context of administrative-territorial units. It was realized that the data provided by different sources differs due to the variation in the calculation methods used, and the lack of detailed regional reports from foreign agencies shows the novelty of the research. In the current research the projection of administrative borders on the territory of oil and gas-geological zoning is used. To determine the degree of the dependence of regional economies from the oil and gas industry, current data about the state of the mineral resource base in the subjects of the Russian Federation were compared with the geography of the location of processing enterprises. On the basis of the obtained materials, the classification of oil and gas-bearing territories is carried out.

The Role of Hybrid IoT with Cloud Computing and Fog Computing to Help the Oil and Gas Industry Recover from Covid-19 and Face Future Challenges

2021 ◽  
Author(s):  
Ethar H. K. Alkamil ◽  
Ammar A. Mutlag ◽  
Haider W. Alsaffar ◽  
Mustafa H. Sabah
Keyword(s):  
Cloud Computing ◽  
Oil And Gas ◽  
Fog Computing ◽  
Petroleum Industry ◽  
Oil And Gas Industry ◽  
Edge Computing ◽  
Cloud Services ◽  
Gas Industry ◽  
Comprehensive Review ◽  
Better Than

Abstract Recently, the oil and gas industry faced several crucial challenges affecting the global energy market, including the Covid-19 outbreak, fluctuations in oil prices with considerable uncertainty, dramatically increased environmental regulations, and digital cybersecurity challenges. Therefore, the industrial internet of things (IIoT) may provide needed hybrid cloud and fog computing to analyze huge amounts of sensitive data from sensors and actuators to monitor oil rigs and wells closely, thereby better controlling global oil production. Improved quality of service (QoS) is possible with the fog computing, since it can alleviate challenges that a standard isolated cloud can't handle, an extended cloud located near underlying nodes is being developed. The paradigm of cloud computing is not sufficient to meet the needs of the already extensively utilized IIoT (i.e., edge) applications (e.g., low latency and jitter, context awareness, and mobility support) for a variety of reasons (e.g., health care and sensor networks). Couple of paradigms just like mobile edge computing, fog computing, and mobile cloud computing, have arisen in recently to meet these criteria. Fog computing helps to optimize services and create better user experiences, such as faster responses for critical, time-sensitive needs. At the same time, it also invites problems, such as overload, underload, and disparity in resource usage, including latency, time responses, throughput, etc. The comprehensive review presented in this work shows that fog devices have highly constrained environments and limited hardware capabilities. The existing cloud computing infrastructure is not capable of processing all data in a centralized manner because of the network bandwidth costs and response latency requirements. Therefore, fog computing demonstrated, instead of edge computing, and referred to as "the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IIoT services" (Shi et al., 2016) is more effective for data processing when data sources are close together. A review of fog and cloud computing literature suggests that fog is better than cloud computing because fog computing performs time-dependent computations better than cloud computing. The cloud is inefficient for latency-sensitive multimedia services and other time-sensitive applications since it is accessible over the internet, like the real-time monitoring, automation, and optimization of petroleum industry operations. As a result, a growing number of IIoT projects are dispersing fog computing capacity throughout the edge network as well as through data centers and the public cloud. A comprehensive review of fog computing features is presented here, with the potential of using it in the petroleum industry. Fog computing can provide a rapid response for applications through preprocess and filter data. Data that has been trimmed can then be transmitted to the cloud for additional analysis and better service delivery.

scholarly journals Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5659
Author(s):  
Haibo Cheng ◽  
Haibin Yu ◽  
Peng Zeng ◽  
Evgeny Osipov ◽  
Shichao Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Domain Knowledge ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Pumping System ◽  
Artificial Lift ◽  
Sucker Rod ◽  
Dynamometer Card

Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

scholarly journals Intelligent system for object recognition on optical images using cascade neural networks

2018 ◽  
pp. 50-55 ◽  
Author(s):  
M. V. Shavranskyi ◽  
A. V. Kuchmystenko
Keyword(s):  
Neural Network ◽  
Oil And Gas ◽  
Intelligent System ◽  
Oil And Gas Industry ◽  
Recognition System ◽  
Structure Model ◽  
Gas Industry ◽  
Emergency Situations ◽  
Optical Images

The paper is devoted to increasing the accuracy of the classification of objects on optical images by developing a structure, model and method of teaching the combined neural network and creating on its basis an intelligent image recognition system for tasks of the oil and gas industry - diagnostics, forecasting of emergency situations of technological objects.

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