Oilfield Processes Research Articles

Study on the unheated gathering boundaries of crude oil during high water content period in cold region

The energy consumption of oil gathering and transportation can be effectively reduced by using an unheated gathering process in oilfields with high water content. However, the phenomenon of “condensate sticking to the wall” may arise due to low temperatures in the cold region, which affects the safe production of this process. In this study, the safe production temperature limit of high water content crude oil in a cold climate without heating is determined by using a combination of a cold finger experiment and a field test. Research using cold finger devices indicates that crude oil’s viscous wall temperature typically below the pour point and diminishes as water content and shear stress rise. Through field tests with gradually decreasing oil–water mixture temperatures, the relationship between the oil–water mixture temperature and pipeline pressure drop is established. The temperature at the pressure drop surge point is then chosen as the limit for unheated gathering and transportation. A model for calculating the viscous wall temperature considering the flow state is established, its average calculation error is 1.44%. This study can provide important guidance for the feasibility judgment of the unheated gathering and transportation process and its safe operation in oil fields in cold regions.

Optimization of scale inhibitor and adsorption enhancer during squeeze process in offshore oilfields

To solve the problem that scale in oilfields can lead to a reduction of formation permeability and blockage of wellbore and production system pipelines, through the scale inhibitor-formation water compatibility experiment and the minimum effective scale inhibitor concentration experiment, three different types of surfactants are selected as adsorption promoters. Three different types of surfactants were selected as adsorption–desorption accelerators through dynamic core adsorption–desorption experiments. The results of the indoor experiment show that the three types of scale prevention agents selected can meet the environmental protection requirements of the sea area. The static scale prevention rate at high temperature (>120 °C) exceeds 80%, and the minimum effective concentration is less than 3.5 mg L−1. The preferred cation Gemini surfactant has a stable adsorption enhancement effect of the scale inhibitor under different water-bearing conditions and can effectively extend the squeezing life under complex formation conditions. The results improve the comprehensive economic benefits of the field and provide technical feasibility to significantly improve the scale prevention efficiency of the field.

AI APPLICATIONS IN RESERVOIR MANAGEMENT: OPTIMIZING PRODUCTION AND RECOVERY IN OIL AND GAS FIELDS

This paper explores various AI applications in reservoir management, highlighting how machine learning, data analytics, and advanced algorithms contribute to optimizing production and recovery in oil and gas fields. From predictive modeling to real-time monitoring and control, AI offers promising solutions to address the industry's evolving demands and enhance operational performance Reservoir management in oil and gas fields has historically presented challenges demanding continuous monitoring, analysis, and optimization for maximizing production and recovery. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. This paper delves into diverse AI applications within reservoir management, emphasizing the pivotal roles played by machine learning, data analytics, and advanced algorithms in optimizing production and recovery processes in oil and gas fields. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. Keywords: Reservoir Management, Oil And Gas Fields, Optimization, Recovery, Machine Learning, Data Analytics, Predictive Modeling.

A TGA-FTIR study on the pyrolysis of oil-based drilling cuttings and the influence of its solids on pyrolysis characteristics

During the drilling process in oil and gas fields, rock cuttings and other solid impurities are returned to the wellhead along with oil-based drilling fluids, forming oil-based drill cuttings. Organic pollutants in oil-based drill cuttings can be removed through pyrolysis. Although there has been some research on the pyrolysis of oil-based drilling cuttings, there is little analysis of the impact of rock-solid impurities in oil-based drill cuttings on the pyrolysis of organic components. Therefore, in this study, thermogravimetry coupled with infrared spectroscopy was used to analyze the pyrolysis characteristics of oil-based drill cuttings, exploring the major volatile components at different pyrolysis temperatures. Meanwhile, the oil and solids in oil-based drilling cuttings were separated by Soxhlet extraction, and the influence of solids on pyrolysis characteristics was studied. The results showed that the average pyrolysis activation energy and pre-exponential factor of the main pyrolysis stage of oil-based drill cuttings obtained by the FWO method were 50.6 KJ/mol and 3.951×104 s−1, respectively, while those of the separated residual oil were 84.7 KJ/mol and 2.905×109 s−1, respectively. This indicates that solids in oil-based drilling cuttings is increasing the mass loss rate during pyrolysis. The difference in pyrolysis kinetics between oil-based drilling cuttings and residual oil indicates that the solid in oil-based rock cuttings provides a surface for the reaction, which provides a reaction site for the decomposition of hydrocarbons to produce lighter volatile hydrocarbons. Additionally, the results of infrared spectroscopy indicated that the volatile products of the pyrolysis of oil-based drilling cuttings include CH4, CO, CO2, H2O, aldehydes, organic acids, etc. CH4 is the main product of the first weight loss stage, mainly released between 150°C and 200°C. With the increase in temperature, the concentrations of H2O, CO2, aldehydes, and organic acids gradually increased. At 600°C, the maximum concentration of CO2 release occurred, and the release of CO2 was the main reason for the third-stage weight loss in the pyrolysis of oil-based drilling cuttings

Chemical Risk Assessment and Prevention Action Plan Related to Petroleum Production. Case Study of Laboratory and Storage Units in an Algerian Oil and Gas Company

Chemical risk is considered one of the most frequent risks in the workplace, affecting the safety of workers continuously exposed to chemical substances. In addition, it can have an environmental impact and cause an explosion when unacceptable conditions are met. To know the effects of chemical risks and how to prevent them, this study is proposed, the main objective of which is the evaluation of chemical hazards and the proposal of an action plan to avoid this kind of risk in industrial companies. Our case study focused on the crude oil production company " PERTAMINA" using the SEIRICH software. The results indicate that most chemicals are hazardous and can cause serious harm to workers exposed to them periodically. Therefore, the proposed preventive action plan for safety improvement aims to ensure continuous development in oil and gas field production, processing, and distribution, taking into account the health and safety of workers.

Study on the Uniform Steam Injection Method of Horizontal Wells during the SAGD Process in Heavy Oil Fields

Study on the Uniform Steam Injection Method of Horizontal Wells during the SAGD Process in Heavy Oil Fields

Artificial neural networks in predicting of the gas molecular diffusion coefficient

The diffusion coefficient is one of the most important parameters for designing two-phase operations between liquid and gas phases in refineries and petrochemical industries, as well as for the gas injection process in oil fields to enhance oil production. Accurate knowledge of this parameter is essential for the prediction of the dissolution rate of the gas phase into the liquid phase. Ideally, this parameter should be obtained experimentally. Given that setting up laboratory equipment and conducting experiments can be costly and time-consuming, mathematical modeling is used as an alternative. In some cases, this data is not either available or reliable, which poses a challenge for designs. Hence, empirically derived correlations are used to predict molecular diffusion coefficients. However, the success of empirical models depends mainly on the range of data at which they were originally developed. Empirical models are not comprehensive for applying to the other data. Recent studies demonstrated that the alternative approach to modeling complex processes and identifying the effective parameters is the artificial neural network (ANN), a suitable prediction method. This study presents a new model developed to predict the molecular diffusion coefficient of methane in crude oil. The model is developed using 172 data points collected from recent literature. Out of the total laboratory data, 90% (155 data points) were used for training the desired neural network, while 10% (17 data points) were reserved for testing and evaluating the performance of the network. The multi-layer perceptron (MLP) neural network architecture with back-propagation (BP) training algorithm was used successfully for the prediction of diffusion coefficients of methane in crude oil. The developed model is compared with the empirical data, which shows the developed model predicts the methane molecular diffusion coefficient in crude oil with an average absolute error of 4.18%.

Chemical Risk Assessment and Prevention Action Plan Related to Petroleum Production. Case Study of Laboratory and Storage Units in an Algerian Oil and Gas Company

Chemical risk is considered one of the most frequent risks in the workplace, affecting the safety of workers continuously exposed to chemical substances. In addition, it can have an environmental impact and cause an explosion when unacceptable conditions are met. To know the effects of chemical risks and how to prevent them, this study is proposed, the main objective of which is the evaluation of chemical hazards and the proposal of an action plan to avoid this kind of risk in industrial companies. Our case study focused on the crude oil production company " PERTAMINA" using the SEIRICH software. The results indicate that most chemicals are hazardous and can cause serious harm to workers exposed to them periodically. Therefore, the proposed preventive action plan for safety improvement aims to ensure continuous development in oil and gas field production, processing, and distribution, taking into account the health and safety of workers.

Evaluating the corrosion index of DTPA at different conditions - key of improving the performance of chelating agents in field treatments

Solutions that rely on chelating agent are widely implemented in upstream oil and gas field processes, due to their unique performance and their ability of capturing specific ions. There is a need to fully understand the impact of chelating agents on operational tools at different conditions. A variety of complications in processing chelating agents are related to their corrosion behavior and iron control capability. In this work, the static corrosion index of chelating agents (DTPA, EDTA, and GLDA) was investigated experimentally over the pH range of 4–12 using different basic media, where the impact of the courter ion (potassium or sodium) is in focus. The impact of preparing the chelating solutions differently on the rate of corrosion was explored. Essentially, the aim was to adjust the pH differently (upwards or downwards, referred to as “Basification” and “Acidification” lines, respectively). The tests were conducted using aging cell isolated by Teflon liner at 500 psi and different temperatures (200 °F, 250 °F and 310 °F). The results show that the dissolution rate of the iron by the chelating agent was influenced considerably by preparation route, that is solutions with the same pH value do not result in the same dissolution rate. For instance, at pH 4.4 and 310 °F test conditions, the corrosion index for the DTPA-K (Basification) was approximately 0.20 lb/ft2, meanwhile, the prepared sample in acidification line (DTPA-K) yielded 0.15 lb/ft2 at same test conditions. Similar observation was exhibited for the other samples. The reaction rate in the upwards preparation process (Basification line) was higher than the rate yielded in the downwards preparation process (Acidification line). We propose a mechanism that accounts the observations that is rooted in the ability of binding external species such as Na+, K+ and Cl− to the central system (either the chelating agent itself or the Fe2+ chelating agent adduct). The mechanism is supported by a series of ab initio calculations.

Long-Term Microbial DNA-Based Monitoring of the Mature Sarukawa Oil Field in Japan

Summary Microbial DNA-based monitoring is a promising tool for reservoir monitoring that has been used mainly for shale reservoir development. In this study, long-term microbial DNA-based monitoring was applied to the Sarukawa oil field, which has a complex reservoir structure with no practical simulation model available. Fluid samples were collected periodically from nine production wells and two injection wells from October 2019 to July 2021. DNA was extracted from the samples, and the microbial composition was analyzed by 16S ribosomal ribonucleic acid (rRNA) gene amplicon sequencing and real-time polymerase chain reaction (PCR). Based on similarities between the microbial profiles, the samples were classified into seven clusters that corresponded closely to the original fluid type (i.e., injection or production fluid) and specific environment (e.g., geological strata or compartments). A comparative analysis of the microbial profiles suggested possible well connectivity and water breakthrough. These results demonstrate that microbial DNA-based monitoring can provide useful information for optimizing production processes (e.g., waterflooding) in mature oil fields.

Non-Darcian Displacement of Oil by a Micellar Solution in Fractal Porous Media

A Buckley–Leverett analysis with capillary pressure to model the oil displacement in fractal porous media is herein presented. The effective permeability for a non-Newtonian micellar fluid is calculated by a constitutive equation used to describe the rheological properties of a displacement fluid. The main assumption of this model involves a bundle of tortuous capillaries with a size distribution and tortuosity that follow fractal laws. The BMP model predicts two asymptotic (Newtonian) regions at low and high shear and a power-law region between the two Newtonian regions corresponding to a stress plateau. Both the stress at the wall and the fluidity are calculated using an imposed pressure gradient in order to determine the mobility of the solution. We analyze different mobility ratios to describe the behavior of the so-called self-destructive surfactants. Initially, the viscosity of the displacing fluid (micellar solution) is high; however, interactions with the porous media lead to a breakage process and degradation of the surfactant, producing low viscosity. This process is simulated by varying the applied pressure gradient. The resulting equation is of the reaction–diffusion type with various time scales; a shock profile develops in the convective time scale, as in the traditional Buckley-Leverett analysis, while at longer times diffusion effects begin to affect the profile. Predictions include shock profiles and compressive waves. These results may find application when selecting surfactants for enhanced oil recovery processes in oilfields.

Research on the Enhancement of the Separation Efficiency for Discrete Phases Based on Mini Hydrocyclone

The economic and efficient treatment of mixed media in offshore produced fluids is of great significance to oilfield production. Due to the small space and limited load-bearing capacity of offshore platforms, some mature multiphase media separation processes in onshore oilfields are difficult to apply. Therefore, high-efficiency processing methods with small-occupied space are required. Mini hydrocyclones (MHCs) are a potential separation method due to their simple structure, small footprint, and high separation efficiency (especially for fine particles or droplets). However, for discrete phases with different densities and sizes, the enhancement rule of the separation efficiency of MHCs is not yet clear. In this paper, numerical simulation methods were used to study the separation performance of hydrocyclones with different main diameters (including conventional hydrocyclones (CHCs) and MHCs) for discrete phases with different densities and particle sizes. Results show that MHC has the optimal enhancement range for oil–water separation when oil-droplet sizes are 60–300 μm, while the optimal enhancement range for silica particle and water separation is 10–40 μm. For other droplet/particle size ranges, the efficiency enhancement effect of MHC is not obvious compared to conventional hydrocyclones. By calculating the radial force of particles in MHC and CHC, the reasons for the enhanced efficiency of MHC are theoretically analyzed. The pressure drop of MHC is higher than CHC under the same feed velocity, which can be improved by connecting CHC with MHC. Additionally, the fluid velocity test experiments based on particle image velocimetry (PIV) were carried out to verify the accuracy of the numerical simulations. This study clarified the scope of application of MHCs to different discrete phase types, in order to provide a basis for the precise application of MHCs.

Analytical characterization of choline chloride in oilfield process waters and commercial samples by capillary electrophoresis

In this study, a rapid and sensitive method using capillary electrophoresis with indirect UV detection and minimal sample pretreatment was developed and evaluated to analyze choline chloride in oilfield process water samples. To improve peak resolution and separation of choline chloride from other cations present in the samples, the addition of a cationic visualization agent in the background electrolyte, imidazole, and a complexing agent, 18-crown-6, were introduced. Other factors affecting separation and sensitivity were also investigated. Under optimized conditions, choline chloride was baseline separated (<7 min) from common cationic adulterants in commercial choline chloride products with a peak resolution of >1.5 between adjacent peaks. The limits of detection (signal-to-noise ratio = 3) and quantitation (signal-to-noise ratio = 10) were 14.7 and 48.9 mg L−1, respectively. The peak area and migration time’s intraday and interday precision (percent RSD) were all <15%, and the recoveries ranged from 79.4% to 115.2% at different spiking levels. Finally, statistical (Student’s t-test) comparison of the choline chloride content data of oilfield process water samples from the proposed capillary electrophoresis (CE) method compare favourably with traditional methods such as liquid chromatography – mass spectrometry (LC–MS) and Reinecke salt gravimetry.

Mathematical Modeling of Oil Spill Dispersion in Marine Waters

During the extraction and transportation of oil in marine waters, oil spills may occur. It not only caused serious pollution in some sea areas but also have a grave impact on the marine environment. Studying the oil dispersion pattern is important for addressing oil spills accurately and timely. Therefore, through the mathematical knowledge, such as continuity equation and momentum equation, we establish a mathematical model for the dispersion of oil spills in marine water. The methods that are used include data statistics, construction of graphs and charts, example analogy, hierarchical analysis, and random calculation. The research and analysis are conducted for the oil spill process in marine oil fields.

A new SAGD comprehensive multi-stage model for oil production using a concave parabola geometry

Steam-assisted gravity drainage (SAGD) has become a widely used thermal method for developing heavy oil reservoirs. The accurate estimation of steam chamber expansion over the entire life of the SAGD process is very important to forecast its performance correctly. In this study, the concave parabola steam chamber geometry analytical model is expanded to include the rise and confinement stage of SAGD. Based on Darcy's law and material balance, analytical models in the rise and confinement stage of the steam chamber were established to estimate the oil production rate with time respectively. The energy balance is then used to estimate the SOR for different stages. The developed analytical models for different stages of SAGD are combined to obtain a comprehensive multi-stage model. The model is then validated against the experimental data obtained by Cheng and Butler and a numerical simulation with CMG-STARS. The comparison proves that the newly proposed model is more accurate than the previous model. After verifying the accuracy of the proposed model, some important production indicators of the SAGD process in different stages, such as oil production rate, cumulative production and effective height, and energy analysis during different stages of the SAGD process are discussed respectively. The influence of different parameters on the whole process of SAGD is discussed finally. The model proposed in this paper is significant for guiding the actual development of SAGD process in oil fields.

IoT-Based Water Management Systems: Survey and Future Research Direction

The Internet of Things (IoT) sustainable solutions are the next generation of methods for managing and monitoring valuable natural resources such as water. Research has focused on smart IoT-based water management and monitoring system designs for various types of applications, including agricultural, industrial, residential, and crude oil exploration sectors. To this end, unlike other surveys available in the literature, this work presents an all-encompassing analysis of 43 papers published between 2014 and 2022 for systems proposed to manage and monitor water in four different sectors: agricultural, industrial, residential, and oilfield sectors. In this work, we also propose a new optical water management system to address a gap in the literature, particularly for oilfield processes that require significant amounts of water to stimulate oil production. The proposed system employs an advanced optical technique for sensing and transmitting collected data. Overall, this work provides a seminal guide for future research efforts focused on integrating IoT in the field of water management and monitoring.

Применение рентгеновской микротомографии керна в нефтепромысловой геологии

The article presents studies devoted to the practical application of computer X-ray microtomography (micro-CT) in oilfield geology. In particular, the authors give results of using the method for sample defectoscopy before petrophysical studies in order to improve the quality of analyzes. The paper includes an example of assessing the depth of core plugging with drilling fluid; assessing the mineral composition by micro-CT; experimental core studies when modeling the thermal effect on the oil source rocks of the Bazhenov formation. The authors also examine the current state of research in the field of digital petrophysics or digital core. The study is aimed at introducing the micro-CT method into the oilfield process.

Experimental Investigation of the Vibration Control of Nonrotating Periodic Drill Strings

Abstract During the drilling process in oil and gas fields, slender drill strings often experience a multitude of complex and simultaneous vibrational phenomena. Drill string vibrations hinder the drilling process and can cause premature wear and damage to the drilling equipment. Here, the suppression of drill string vibrations during drilling operations is experimentally investigated using a novel drill string design, based on the use of innovative periodic inserts that control the vibration transmissibility in different directions. These inserts are equipped with viscoelastic rings that act as sources of local resonances, surrounding piezoelectric actuators that generate internal axial loading when electrically excited. An experimental prototype that combined all these details was constructed and tested to demonstrate the periodic drill string's feasibility and effectiveness in minimizing undesirable vibrations. The obtained results indicate that the periodic inserts’ careful design can effectively enhance the drill strings’ dynamic behavior and conveniently regulate its bandgap characteristics. Both radial and axial vibrations were controlled, and the vibrations’ amplitude was reduced significantly over a wide range of frequencies. The proposed approach appears to present a viable means for designing intelligent drill strings with tunable bandgap characteristics.

Development of neural network for control production process in oil and gas fields

For a long time there has been a tendency to increase field productivity, therefore, increasing oil recovery is the main task for fuel and energy complex. Currently, neural networks are increasingly used in various industries. The advantage of neural networks is to work with a large amount of data, however, it must have sufficient data sets collected and prepared for its operation, thereby achieving high decision accuracy. When developing oil and gas fields, the main task is to ensure maximum production from an economic and physical point of view. Oil production at oil and gas fields varies in volume, complexity, operating conditions, etc., therefore, it is necessary to find the optimal production conditions for each field. At the moment, the main problems in oil production at oil and gas fields are: the long processing time of data collected from wells, the increased risks of operating these wells, as well as the low amount of oil produced. The main objective of this study is to develop a control method us in artificial intelligence to control the production process in oil and gas fields, taking in to account all factors, in order to maximize oil production. In the course of this study, direct transmission to the neural network was obtained, which allows oil to be extracted at oil and gas fields, taking into account all factors. The resulting neural network, without reconfiguring weighted connections, generates output signals when applied to the input to the network. The resulting neural network expresses the patterns that are present in the input data. This network turns out to be the functional equivalent of some model of dependencies between variables.

Primary cementing of horizontal wells. Displacement flows in eccentric horizontal annuli. Part 1. Experiments

Abstract