Petroleum Equipment Research Articles

Study on corrosion behavior of impeller of oil pump with CO2 crude oil

Abstract CO2 was one of the key factors that caused serious corrosion of petroleum equipment and facilities. Aiming to address the problem of serious pitting corrosion in the impeller of an oil pump in a CO2 flooding field, immersion, and electrochemistry experiments were comprehensively analyzed in this paper. The results showed that the corrosion rate of FV520B steel increased exponentially with the increase of pipeline transport pressure and flow rate (R2>0.9), and the potential shifted negatively. With the increase of pipe pressure, the corrosion directivity of the specimen surface gradually disappeared, showing a distinct pitting area and a comprehensive corrosion area. This was due to cavitation under high pipe pressure conditions.

An interesting phase transition in polyamide 6/nitrile rubber blend induced by temperature and pressure and its effect on tribological performance

AbstractThe preparation of high‐performance rubber‐plastic blending elastomers has always been a research hotspot in the field of petroleum equipment. The two‐phase separation structure of the blend determines the macroscopic properties of the materials. It is of great significance to deeply explore the influence of processing technology on the two‐phase separation structure. In this paper, polyamide 6 (PA6)/nitrile rubber blends were prepared via the melt–compounding method. To meet the requirements of oil production and oil transport equipment, we investigated systematically the mechanical properties of the specimens exposed to different environments. The oil resistance and high‐temperature resistance of the blends were enhanced considerably with the addition of PA6. The temperature and pressure fields induced a novel transition from particle into fibrous phase based on Ostwald's ripening. However, this brand new transformation showed no benefits to the improvement of the frictional performance. Meanwhile, the relationship between aggregate structure and mechanical performance was demonstrated from the aspects of crystallization behavior and phase morphology.

Study the Corrosion Inhibition on the Iraqi Fuel Tanks using Cefoperazone Drug

Corrosion is one of the most important problems that face the petroleum industry, both production and refining, and cause damage to petroleum equipment, tanks, and transmission lines, and increasing maintenance costs. in this study , was adding six concentrations of the drug expired Cefoperzone (Exp CEF) (50, 100, 200, 300, 400, and 500 ppm) to a Simulated Oil Well Water (SOWW), the inhibitive role of (Exp CEF) drug was investigated to Reduce the corrosion risk of carbon steel. Four different temperatures were used to conduct electrochemical tests (303, 313, 323 and 333 K) to achieve the study. The study was then supported by an examination of the inhibited surface using field emission scanning electron microscopy with energy dispersive (FESEM) and (AFM). The results indicated occurring the inhibition by an anodic inhibitor that controls the dissolution of iron from steel gave the highest inhibition efficiency (IE%) by adding 500 ppm, while the calculation of polarization resistance (Rp) gave the highest resistance by 400 ppm through adsorption the added drug to show flakes shaped structures with decreasing surface roughness (Ra ) from 307.1 to 83.15 after inhibition. The calculation of adsorption isotherm confirmed the obeying of Langmuir adsorption isotherm by giving the coefficient of linear regression was about one in the range of 0.999 > R^2 > 0.995, with the spontaneous adsorption that estimated from the negative values of 〖∆H〗_ads^o and variation in the type of adsorption to be physically or chemically according to the added concentration because of the differential sign of 〖∆H〗_ads^o . The sign of 〖∆H〗_ads^o was positive that reflecting the increase in randomness at the metal/solution interface.

An Insight into the Prediction of Scale Precipitation in Harsh Conditions Using Different Machine Learning Algorithms

Summary Scale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this fact, the prediction of scale precipitation has vital importance among petroleum engineers. In the current work, different intelligent models, including the decision tree, random forest (RF), artificial neural network (ANN), K-nearest neighbors (KNN), convolutional neural network (CNN), support vector machine (SVM), ensemble learning, logistic regression, Naïve Bayes, and adaptive boosting (AdaBoost), are used to estimate scale formation as a function of pH and ionic compositions. Also, a sensitivity analysis is done to determine the most influential parameters on scale formation. The novelty of this work is to compare the performance of 10 different machine learning algorithms at modeling an extremely non-linear relationship between the inputs and the outputs in scale precipitation prediction. After determining the best models, they can be used to determine scale formation by manipulating the concentration of a variable in accordance with the result of the sensitivity analysis. Different classification metrics, including the accuracy, precision, F1-score, and recall, were used to compare the performance of the mentioned models. Results in the testing phase showed that the KNN and ensemble learning were the most accurate tools based on all performance metrics of solving the classification of scale/no-scale problem. As the output had an extremely non-linear behavior in terms of the inputs, an instance-based learning algorithm such as the KNN best suited the classification task in this study. This argumentation was backed by the classification results. Furthermore, the SVM, Naïve Bayes, and logistic regression performance metrics were not satisfactory in the prediction of scale formation. Note that the hyperparameters of the models were found by grid search and random search approaches. Finally, the sensitivity analysis showed that the variations in the concentration of Ca had the highest impact on scale precipitation.

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.

Evaluation of Breakthrough Technological Innovation Capability of Petroleum Equipment Manufacturing Enterprises Based on the CFPR‐TIFNs Model

“Internet + innovation” is a new emerging paradigm for breakthrough technological innovation, which is more complex than traditional scenarios because “Internet +” has completely overturned the acquisition of creativity and knowledge. This article aims to evaluate the breakthrough technological innovation capability (BTIC) of petroleum equipment manufacturing enterprises under the new paradigm of “Internet + innovation.” This article constructs an index system that can reflect the characteristics of “breakthrough” and “Internet +” in five dimensions. Due to the difficulties of conducting quantitative measurements for the novel index system, a hybrid evaluation model based on a consistent fuzzy preference relationship (CFPR) and triangular intuitionistic fuzzy numbers (TIFNs) is put forward for the evaluation of BTIC. The main contribution of this article is to propose an index system and model for the evaluation of BTIC under the “Internet + innovation” paradigm, which is conducive to improving the quality of breakthrough technological innovation activities under more complex scenarios. The CFPR‐TIFNs model can not only effectively calculate the BTIC of petroleum equipment manufacturing enterprises but also deeply analyze the influencing factors for their relatively weak technological innovation capability. This article provides a new methodology to solve problems in a more complex technological innovation system.

Investigation on anisotropic tribological properties of superhydrophobic/superlipophilic lead bronze surface textured by femtosecond laser

Superhydrophobic and superlipophilic surfaces have been widely used in rail transit, petroleum equipment, aerospace, and other fields in recent years. Most of these surfaces have the disadvantage of poor wear resistance. However, the surface of the suitable microstructure has antifriction and wear-resistant properties. The femtosecond laser is easy to control, has good repeatability, and has great advantages in preparing special wetted surfaces with microstructures. In this work, an ultrafast femtosecond laser is used to prepare a superhydrophobic/superlipophilic lead bronze surface. It is found that the sample immediately after laser processing has a high content of surface polar bonds and high free energy, which is a super-hydrophilic surface. After being placed in the air for two months, the adsorption of hydrocarbons on the surface increases the C-C/C-H non-polar bond content, and the surface free energy decreases, showing superhydrophobic/superlipophilic characteristics. The friction behavior of superhydrophobic/superlipophilic surfaces in oil lubrication and dry friction is different, and the anisotropy of the surface structure will also lead to the anisotropy of the tribological properties. There mainly occurred fatigue wear. This work provides a method for the preparation of anisotropic antifriction and wear-resistant superhydrophobic/superlipophilic surfaces, and variable surface microstructures could be prepared for various friction conditions.

Petroleum Equipment Industry to Digitalization—Taking Jereh Equipment as An Example

The Report on the Work of the Government 2021 voiced that China will strive to develop the Internet in the next few years. It is the fourth year that the industrial Internet has been written in the government report. With the combination of national promotion and social development needs, China’s industrial Internet has experienced unprecedented growth in recent years. Based on the case analysis of Jereh’s IOT, combining theory and practice, this paper studied the development trend of the petroleum equipment industry’s digital transformation and the application scenarios of the Industrial Internet of Things. Through realizing intelligent industrial operations by using the data analysis results obtained by business transformation of machine, computer, and personnel, and based on the application of Internet of Things technology, Jereh’s IOT aims to promote the high integration of enterprise informatization, equipment informatization and operations process informatization, so as to achieve digital and lean industry management.

Self-Learning Controllers in the Oil and Gas Industry

Recently, solving the optimization-control problems by using artificial intelligence has widelyappeared in the petroleum fields in exploration and production. This paper presents the stateof-the-art reinforcement-learning algorithm applying in the petroleum optimization-controlproblems, which is called a direct heuristic dynamic programming (DHDP). DHDP has twointeractive artificial neural networks, which are the critic network (provider acritique/evaluated signal) and the actor network (provider a control signal). This paper focuseson a generic on-line learning control system in Markov decision process principles.Furthermore, DHDP is a model-free learning design that does not require prior knowledgeabout a dynamic model; therefore, DHDP can be appllied with any petroleum equipment ordevise directly without needed to drive a mathematical model. Moreover, DHDP learns byitself (self-learning) without human intervention via repeating the interaction between anequipment and environment/process. The equipment receives the states of theenvironment/process via sensors, and the algorithm maximizes the reward by selecting thecorrect optimal action (control signal). A quadruple tank system (QTS) is taken as a benchmarktest problem, that the nonlinear model responses close to the real model, for three reasons:First, QTS is widely used in the most petroleum exploration/production fields (entire system orparts), which consists of four tanks and two electrical-pumps with two pressure control valves.Second, QTS is a difficult model to control, which has a limited zone of operating parametersto be stable; therefore, if DHDP controls on QTS by itself, DHDP can control on otherequipment in a fast and optimal manner. Third, QTS is designed with a multi-input-multioutput (MIMO) model for analysis in the real-time nonlinear dynamic system; therefore, theQTS model has a similar model with most MIMO devises in oil and gas field. The overalllearning control system performance is tested and compared with a proportional integralderivative (PID) via MATLAB programming. DHDP provides enhanced performancecomparing with the PID approach with 99.2466% improvement.

Investigations on the Microstructure and Degradation Behavior of Hollow Glass Microspheres/Mg Alloy Composites

Degradable materials with low density exhibit unique applications in the petroleum equipment field. Herein, hollow glass microspheres (HGM)‐reinforced Mg alloy degradable composites are fabricated. The microstructure and degradation behavior of the composites are investigated using scanning electron microscopy, immersion tests, and electrochemical measurements. Then the influence mechanisms of the HGM on the degradation property of composites are further revealed. The results show that the addition of the HGM significantly reduces the density of the composites. Different degrees of chemical reaction between the Mg alloy melt and HGM wall happen during the fabrication of the composites, resulting in the formation of MgO and Mg2Si. From the corrosive morphologies of the composites, Mg2Si phase can serve as a microgalvanic cathode consequently inducing more galvanic corrosion. The interface between HGM and matrix alloy can serve as the initiation site for pitting corrosion and increase the exposed area of the matrix in the aggressive medium. The electrochemical measurements prove that the HGM can act a part in facilitating the exfoliation of degradation products. Consequently, the HGM greatly increases the degradation rate of the composites in the KCl solution. The current study provides a new insight for developing lightweight Mg‐based composites with quickly degradable performance.

Discussion on Case Teaching Method of Mechanical Majors in Petroleum Colleges Under New Situation

At present, new knowledge in the field of mechanical engineering and petroleum equipment is emerging, which puts forward requirements for increasing teaching knowledge points. Engineering education professional certification also requires controlling the theoretical class hours on the premise of student-centered. In order to explore the teaching strategies of mechanical specialty in petroleum industry colleges and universities under this new situation, the new knowledge in the field of mechanical engineering and the frontier development trend in the field of petroleum machinery and equipment are combed, as well as the requirements of the current machinery industry for personnel training are analyzed. The case teaching method of integrating new mechanical engineering knowledge into petroleum machinery professional courses is proposed, which can teach students more knowledge points without increasing the class hours, and train students’ ability to apply multi-disciplinary knowledge to solve complex engineering problems according to professional certification requirements comprehensively. Combined with two courses of the oil and gas field downhole equipment and oil and gas field hydraulic machinery, the teaching case of pipe string with special thread and plunger pump is designed by using the method in this paper, and the practice in the teaching has received a good effect. The study recognizes that the integration of mechanical engineering new knowledge into the course of petroleum machinery specialty and the use of case teaching method can effectively solve the contradiction between the increase of knowledge points and the control of school hours under the new situation, which is conducive to the cultivation of mechanical engineering talents to meet the requirements of industry development. This method can be used as a reference for the training of mechanical professionals in other industrial colleges and universities.

Modeling and analysis of structure parameters on the lifting force for power catwalk

PurposeAs the excessive lifting force can lead to catwalk rollover and well site accidents, the lifting process boundary conditions and structural parameters have a significant effect lifting force, it is important to analysis the structural parameters on the maximum lifting force in the lifting process of power catwalk.Design/methodology/approachA new model is proposed to analyze the influence of structure parameters on its lifting force for lifting power catwalk in this paper, and the geometric and dynamic equations are established according to the different boundary conditions in different stages. In addition, the establishment of dynamics equations is based on D'Alembert's principle. To solve the model, dynamic analysis software is developed, which uses c # call MATLAB to solve the geometric and dynamic equations. The maximum lifting force is analyzed and optimized according to the software, the influence of structural parameters on the maximum lifting force is obtained and the correctness of the optimization is proved by experiments.FindingsThe best value of offset e is 0. The length of L22 should as small as possible while the installation size of the end of the conveying arm are guaranteed. The length of L1 should as small as possible while ensuring the not exceed the maximum value. The maximum lifting force remain the same in the second phase, the maximum lifting force decreases with the increase of Lcp, Lcpshould as small as possible. The maximum pressure of the hydraulic oil dropped by an average of 13.62% under optimized parameters.Practical implicationsThis paper provides a theoretical basis for the selection of hydraulic winch, which also provides the theoretical basis and data support for the design and optimization of the structural parameters of the power catwalk.Social implicationsThis research has industrial applications in SJ Petroleum Machinery CO.LTD, SINOPEC (China) .CANRIG, North Rig, TESCO, Sichuan HONGHUA petroleum equipment CO.LTD of CNPC., Baoji Oil field Machinery CO.LTD, SJ Petroleum Machinery Co. LTD of SINOPEC, Yantai Jereh Oilfield Services Group CO.LTD, Nanyang clips oil equipment (group) CO. LTD, etc are the likely users.Originality/valueA new model is proposed to analyze the lifting force of lifting power catwalk. The model takes into account the inertia force of the structure, development of dynamics software and analysis and optimization of structural parameters. The maximum lifting force is analyzed and optimized according to the software, the influence of structural parameters on the maximum lifting force is obtained and the correctness of the optimization is proved by experiments.

Analysis of Leakage Model of All-Metal Screw Pump

Traditional rubber screw pumps use an interference method to engage. When the pressure is less than the breakdown pressure of the pump, there is no leakage in the pump. The all-metal screw pump stator and rotor are made of metal, and the stator and rotor adopt a gap-fitting engagement method, so even when the pump is working normally, the leakage is objective. Based on the concentric annular gap flow, the pressure drop leakage caused by the fluid inertia force is fully considered, and the calculation formula of the leakage of the all-metal screw pump is studied from the three aspects of transverse leakage, longitudinal leakage, and oblique leakage. This model was experimentally verified by a commercial all-metal screw pump produced by Shihong Petroleum Equipment Company. The model results show that the leakage of the all-metal screw pump is mainly affected by the structure parameters of the pump itself and the density of the pumped fluid, and the gap height is the main factor affecting the leakage.

Intelligent diagnosis of petroleum equipment faults using a deep hybrid model

Performance assessment and timely failure detection of the electric submersible pump can reduce operation costs and maintenance in the oil and gas field. Features of equipment malfunction are changes in vibration signals. Evaluation of vibrations based on accelerometer sensors can detect failures and allows assessment of system failures. This paper proposes a reliable deep learning-based method for electric submersible pump faults detection. The frequency, time and spectral information of the vibrational signal are considered as input to the deep hybrid model. The spectral information includes the spectrogram obtained using the short-time Fourier transform and the scalogram as a result of the continuous wavelet transform and provides a detailed study of the vibration signal. The proposed approach is compared with k-nearest neighbors, support vector machines, logistic regression, and random forest. The experimental evaluation shows that the proposed deep hybrid model is superior to these machine learning methods, and can automatically and simultaneously detect failures of the electric submersible pump according to the vibration signal that is generated during system operation. The proposed approach gives good results and can help an expert in automatic diagnostics of equipment and several complex technical systems.

Effects of Network Closure on Cooperative Innovation: Evidence from Dongying’s Petroleum Equipment Industry in China

There are two opposing viewpoints on which kind of network configuration provides a more competitive advantage, namely, network closure or structural holes, with the latter occupying the dominant position in the literature. Using social network analysis and negative binomial regression methods, we graph the co-patent network of Dongying’s petroleum equipment industry in China and explore its impact on enterprise innovation. The analysis is based on 17 face-to-face interviews, 31 enterprise questionnaires, and 354 co-patent records from the China State Intellectual Property Office identifying cooperative innovation for the years 1988–2013. We find that this network is closed, controlled by state-owned enterprises, and its closure has positive effects on enterprise innovation performance. This may be related to China’s unique industrial development history, state system and policies, regional culture and circumstances, and enterprise characteristics. Therefore, for some industries in specific regions, the advantages usually attributed to structural holes and open innovation may not necessarily apply.

Intelligently Informed Control Over the Process Variables of Oil and Gas Equipment Maintenance

This article details a new technique that uses intelligent methods in order to identify non-standard errors when controlling the technological process of maintenance petroleum equipment. First, a new formulation of the technological process control problem for the maintenance of petroleum equipment is presented. This is stated in terms of classifying errors introduced by measuring the parameters of the technological process. Intelligent methods have been proven as a tool to solve the classification problem. Various machine-learning methods have been considered: decision trees, artificial neural networks (ANN), and fuzzy logic. In this study, an effectiveness comparison of the proposed methods has been conducted using experimental data of petroleum equipment maintenance. Results indicate that ANN is the most efficient method to classify measurement errors. The proposed method will primarily improve repair quality of certain equipment components such as the pipeline system for transferring raw hydrocarbon materials. Moreover, it will improve the quality of maintenance work and durability of the pipeline system, which in turn can increase the hydrocarbon production efficiency.

The Experimental Simulation Technology and System of Solid Fluidization Exploitation of Marine Non-Diagenetic Natural Gas Hydrate

The Experimental Simulation Technology and System of Solid Fluidization Exploitation of Marine Non-Diagenetic Natural Gas Hydrate

Permian Pulse: Using Satellite Imagery Analytics To Track the World’s Busiest Oil Play

A trip on Highway 285 between Fort Stockton, Texas, and Carlsbad, New Mexico, is both fascinating and horrifying for the average commuter in the US desert Southwest. The 140-mile stretch of two-lane road can take hours to traverse on a given day as trucks hauling crude oil, sand, water, chemicals, and petroleum equipment overload the battered, sun-fried pavement. The mostly flat, dusty landscape is populated by drilling rigs, workover rigs, and gas flares as far as the eye can see. Even on the highway’s best day, most traffic laws are ignored as oilfield workers in pickup trucks race from site to site. The industrial chaos along the corridor serves as a snapshot of the now multiyear industry boom in the Delaware Basin and greater, 75,000-sq-mile Permian Basin. Keeping track of it all has become a massive challenge for operators, service firms, and midstream companies that make the basin move. They work in an environment that is changing daily, and they can’t afford to be caught by surprise when hydraulically fracturing a well, building infrastructure, delivering equipment to a remote area, or prospecting for new business. An emerging solution: an intelligent, continually updated view from above—or satellite imagery analytics. Companies such as Sourcewater, Planet, and West-wood Global Energy Group are stepping up to monitor the world’s busiest oil play by leveraging relatively inexpensive satellite technology and machine learning. Application of satellite imagery in the oil and gas industry is nothing new. It has been used by companies to monitor remote facilities. Market observers have used the technology to assess global oil supplies by tracking tanker traffic and storage levels. The World Bank has used it to estimate emissions from the flaring of natural gas and oil output by the Islamic State. The real breakthrough is in merging this capability with artificial intelligence and advanced analytics to aid planning for development, production, and logistics while providing competitor intelligence and market research. Development is still in the early stages as companies refine their models to produce the most reliable information possible, and products are just now being rolled out to clients. Finding Water in the Desert An early entrant in the satellite imagery analytics space, Sourcewater’s primary objective is to serve as an online data analytics platform and marketplace for oilfield water sourcing, recycling, and disposal. Founded in 2014 as part of MIT’s Energy Ventures program, Sourcewater “is really about providing oilfield water market intelligence” to its clients, which often are operators planning their completions and production work and logistics or midstream water companies determining where to add new capacity, explained Josh Adler, Sourcewater chief executive officer. Relatively new to the industry himself, Adler’s career path includes founding one of the Internet’s first match-making sites and serving as chief speechwriter to former US Treasury Secretary Paul O’Neill.

American Kestrel Nonbreeding Roost Use and Behavior in South Texas

Roost sites are likely an important component of the American Kestrel's (Falco sparverius), kestrel(s) hereafter, nonbreeding habitat, as quality roost structures can provide thermal cover and protection from predators. We studied winter roost-site use by kestrels in an agricultural landscape in South Texas, where the population is composed of a combination of territorial wintering birds and migrating individuals passing through. Through observations of color-marked and unmarked birds, we identified 50 roost sites during September–March, 2014–2017. Kestrels utilized foliated shrubs and trees as roosting sites (n = 24), as well as various human-made structures (n = 26) including buildings, petroleum and farm equipment, and electrical structures for roost sites. Roost sites varied greatly in the level of concealment and exposure, with seven kestrels roosting on electric structures that allowed high levels of exposure to weather and potential avian predators. We observed aggression between kestrels around roost sites, and we documented small communal kestrel roosts. Because we observed roosting in sites exposed to weather and predators, aggression around roost sites, and communal roosting, we speculate that roosting sites suitable for kestrels may be limited or poorly dispersed in this agricultural landscape.

Analytical Model for the Flow in Progressing Cavity Pump with the Metallic Stator and Rotor in Clearance Fit

As the metallic stator progressing cavity pump (PCP) operates with the stator and rotor in clearance fit, the slippage between cavities has a significant influence on the pump performance. In this paper, an analytical model is developed for the flow in the metallic stator PCP. Based on the analyses of the meshing movement and the clearance geometry inside the pump, the slippage through the transversal and longitudinal sealing regions is calculated considering different slippage mechanisms. Then the flow rate is obtained by subtracting the total slippage from the theoretical volumetric rate. This model is validated against results obtained from the performance experiments of commercial metallic stator PCP products from Shihong Petroleum Equipment Company. The model results show that the metallic stator PCP with smaller clearance or more stages is more capable of achieving good performance at high differential pressure. It is suitable for pumping the fluid with certain viscosity, and the influence of the slippage can be compensated by adopting appropriate high rotational speed. Furthermore, the model can be used to predict the pump performance and provide guidance for the pump design and performance optimization in field applications.