Chinese Oil Field Research Articles

Methodology and Uncertainty Analysis of Methane Flux Measurement for Small Sources Based on Unmanned Aerial Vehicles

The top–down emission rate retrieval algorithm (TERRA) method for calculating the net flux out of a box has been employed by other researchers to assess large sources of methane release. This usually requires a manned aircraft drone with powerful performance to fly over the boundary layer. Few studies have focused on low-altitude box sampling mass balance methods for small sources of methane release, such as at maximum flight altitudes of less than 100 m. The accuracy and sources of uncertainty in such a method still need to be determined as they differ from the conditions of large sources. Nineteen flights were conducted to detect methane emissions from Chinese oil field well sites using a measurement system consisting of a quadcopter and methane, wind speed, wind direction, air pressure, and temperature sensors. The accuracy and uncertainty of the method are discussed. The average absolute relative error of the measurement is 18.5%, with an average uncertainty of 55.75%. The uncertainty is mainly caused by the wind speed and direction, and the background CH4 concentration. The main paths to reduce uncertainty and improve accuracy for low-altitude box sampling include subtracting the background concentration during flux retrieval, enhancing the accuracy of methane measurements, selecting a period of downwind dominant or wind direction change of less than 30 degrees, and ensuring a maximum flight height greater than 50 m with a horizontal distance from the pollution source center of less than 75 m. The results show that TERRA-based low-altitude box sampling is suitable for quantifying methane release rates from small sources.

Removal mechanism of adhering heavy oil from pipeline wall in low-temperature flow

With the global emphasis on heavy oil development and increasing water content in Chinese oilfields, the low-temperature flow of heavy oil has been widely concerned. Integrating experiments and theoretical modeling, a hydrodynamic balance model for removing adhering heavy oil from pipeline wall was proposed to determine the temperature threshold for heavy oil transportation (adhesion temperature, AT). Two forms of heavy oil removal from pipeline wall were identified in the study: slide and lift-off. At low temperatures, the abrupt increase in wall-adhesion mass is primarily due to the inability of oil droplets to be removed through lift-off, but rather only through slide. Therefore, the temperature marking the transition from lift-off to slide is suggested as the AT criterion. Predicted AT values were in effective concordance with experimental values, with a maximum deviation of 3.78 °C. ATs for oilfields were predicted, and field cooling transportation tests showed no significant pressure drop increase, indicating the model's practical applicability. Additionally, the influence factors of the model were analyzed based on considerations of adhesion force and hydrodynamic removal force. This study aims to reduce heating energy consumption and carbon emissions during transportation under the premise of ensuring safe flow of high water-content heavy oil.

Real-time prediction of ROP based on GRU-Informer

Accurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs. ROP prediction is influenced by multiple factors, making accurate prediction challenging. Current research primarily relies on historical data for training and modeling, lacking methods for real-time ROP prediction. This paper introduces a GRU-Informer model for real-time ROP prediction. The model employs GRU (Gated Recurrent Unit) neural networks at the lower level to capture short-term correlations in drilling parameters and uses the Informer model at the top to address long-term dependencies among drilling parameters. Thus, the GRU-Informer can capture both short-term and long-term time dependencies, providing better ROP predictions. This paper constructs a dataset using historical data from a southwestern Chinese oil field for experimentation. RMSE (Root Mean Square Error), MAE (mean absolute error) and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} (Coefficient of Determination) are employed as evaluation metrics for the model. Experimental results demonstrate that the GRU-Informer outperforms traditional recurrent neural networks like LSTM (Long Short-Term Memory), GRU neural networks and Informer in real-time ROP prediction, indicating its practical value.

Structural and compositional diversity of biosurfactants produced by a novel strain of Sporosarcina luteola ME44 from oil reservoir.

The urealytically active microorganism Sporosarcina luteola induces the precipitation of metals, which has attracted attention in biomineralization, bioremediation, and industrial waste recycling. Herein, we report a novel biosurfactant-producing strain of S. luteola ME44 isolated from Chinese Oilfield. The structure, composition, and surface activity of the biosurfactants produced by S. luteola ME44 were investigated by using a combination of the high-performance liquid chromatography, time-of-flight mass spectrometry, and surface tensiometer. The biosurfactant extracted by strain ME44 was identified as surfactin with five variants and the yield was 1010±60mg⋅L-1 . This is the first report on the structural composition and surface activity of biosurfactants isolated from the S. luteola. It extended our knowledge about the role of the species S. luteola in the ecosystem of extreme natural environments such as oil reservoir. In addition, S. luteola ME44 showed bioprecipitation properties for metal ions Cd(II), Cu(II), Zn(II), and Ag(I), which indicated the application potential of S. luteola in the field of bioremediation.

Prediction of Lost Circulation in Southwest Chinese Oil Fields Applying Improved WOA-BiLSTM

Drilling hazards can be significantly decreased by anticipating potential mud loss and then putting the right well control measures in place. Therefore, it is critical to provide early estimates of mud loss. To solve this problem, an enhanced WOA (Whale Optimization Algorithm) and a BiLSTM (Bidirectional Long Short Term Memory) optimization based prediction model of lost circulation prior to drilling has been created. In order to minimize the noise in the historical comprehensive logging data, a wavelet filtering technique was first used. Then, according to the nonlinear Spearman rank correlation coefficient between mud loss and logging parameter values from large to small, seven characteristic parameters were preferred, and the sliding window was used to extract the relevant data. Secondly, the number of neurons in the first and second hidden layers, the maximum training time, and the initial learning rate of the BiLSTM model were optimized using the enhanced WOA method. The BiLSTM network was given the acquired superparameters in order to improve the model’s ability to predict occurrences. Finally, the model was trained and tested using the processed data. In comparison to the LSTM model, BiLSTM model, and WOA-BiLSTM model, respectively, the improved WOA-BiLSTM early mud loss prediction in southwest Chinese oil fields suggested in this study beat the others, receiving 22.3%, 18.7%, and 4.9% higher prediction accuracy, respectively.

Oil-Net: A Learning-Based Framework for Working Conditions Diagnosis of Oil Well Through Dynamometer Cards Identification

Working conditions identification of oil well relies on high-cost and slow-speed artificial inspection in past decades. Working conditions diagnosis through dynamometer cards identification based on machine learning has significant economic value and environmental meaning for oil production. In this paper, we collect dynamometer cards from the famous Chinese Shengli Oil Field as research data. We firstly investigate properties of dynamometers under different working conditions and create a dynamometer card dataset. Further we introduce calibration/visualization preprocess methods, and propose the Oil-Net 1D/2D identification models from the time-series and computer vision views respectively. Experiment results indicate that comparing with other machine learning and time series classification methods, Oil-Net 1D/2D improve identification accuracy significantly. This study provides guidance for the design and implementation of learning-based oil well working conditions intelligent diagnosis system.

Evaluation of enhanced oil recovery methods for mature continental heavy oil fields in China based on geology, technology and sustainability criteria

Enhanced oil recovery (EOR) methods in mature oil fields play an irreplaceable role in supporting the long-term and stable production of crude oil. This study establishes a unified and comparable evaluation index system based on geology, technology and sustainability, re-evaluates the existing EOR projects focused on heavy oil extraction by utilizing hybrid AHP-CRITIC-FCE models, and selects the most efficient, economic, and low-carbon footprint EOR technologies to facilitate the development of mature Chinese heavy oil fields. Three main criteria and nineteen subcriteria are considered in the multi-criteria decision-making system. The weights given to criteria are determined by combining the subjective AHP and objective CRITIC methods. The fuzzy comprehensive evaluation method is adopted to prioritize the performance of the EOR process. Empirical analysis is conducted on seven typical heavy oil technologies. The study finds that geological factors account for the largest proportion, and the top five indicators in terms of weight are buried depth, recovery factor, viscosity, operating cost per tonne of oil and reservoir thickness. Three technologies are recommended for potential future green footprint heavy oil EOR methods. This study combines both expert judgment and field data appraisal to help decision makers better plan the future strategic layout of EOR technology.

Study on Well Pattern System Optimization of Fuyang Reservoir in H Oilfield

The selection of well pattern system is not only the key content in the process of oilfield development, but also the core problem in the design of oilfield development. It is also the basis for the effective utilization of tight reservoir technology. Through scientific analysis of the geological structure and dynamic characteristics of Yang reservoir in H oilfield, the well pattern system can be reasonably optimized and the development of Chinese oilfield can be vigorous. Starting from the selection of well pattern system for oil and gas field development, this paper analyzes the optimization of well pattern system parameters of Fuyang reservoir in H oilfield.

Review: Oil Spaces: Exploring the Global Petroleumscape

Review: <i>Oil Spaces: Exploring the Global Petroleumscape</i>

Storage Ratio of CO2 Miscible Flooding in Chang 8 Reservoir of H Block in Ordos Basin Under Different Injection Methods

Carbon dioxide is a typical kind of greenhouse gases, and the oil and gas field recovery ratio could be significantly improved by injecting it into the formation. The main goal is to increase the recovery ratio in the early stage of studying gas injection miscible flooding. With the advancement of the “carbon neutral” strategy in China, the application of CO2 miscible flooding technology, which aims to enhance the oil recovery ratio in Chinese oilfields, has been further expanded. The development concept needs to change from simply improving the recovery ratio to improving both the recovery ratio and the CO2 storage ratio. In the current CO2 flooding research, people mainly focus on the improvement of the recovery factor, and less attention is paid to the underground storage of CO2. In order to further study the relationship between the displacement efficiency of CO2 and the storage situation, this paper innovatively combines the storage ratio and storage amount of CO2 with the recovery factor. A number of parameters under different injection methods in the study area were calculated, providing a new research angle for the study of CO2 displacement. Take Chang 8 reservoir in Block H of Ordos Basin as an example. Firstly, the minimum miscibility pressure (MMP) of CO2 at formation temperature was measured by the slim tube experiment according to the reservoir pressure status to judge the CO2 flooding status in the study area. Then, the numerical simulation model of the study area was established, and the historical fitting was completed according to the existing CO2 flooding test wells. The reservoir numerical simulation is used to study the recovery ratio and CO2 storage ratio under different CO2 miscible flooding, including different injection rates, injection sequences, and bottom hole pressures. The results show that the MMP of CO2 in the study area is between 12.65 and 14.80 MPa, which means the CO2 flooding state in this area is miscible flooding with high oil displacement efficiency. The CO2 storage ratio at 5–40 t/d CO2 injection rate is between 54.02% and 64.38% after 20 years, and the CO2 storage ratio is larger when the gas injection rate is 10 t/d. In combination with CO2 storage capacity and the oil recovery factor, it is recommended that the gas injection rate of a single well in the study area is 20–30 t/d. The CO2 storage ratio after 20 years under different gas injection sequences is 57.69%–61.27%. It is suggested that the study area should be injected with water and gas alternately after 2 years of gas injection. When continuous gas injection is used, the CO2 storage ratio under different production well bottom hole pressures is between 54.95% and 59.82%. It is recommended that the bottom hole flow pressure in the study area be maintained at between 9 and 10 MPa. The results show that the main factor that affects the annual CO2 storage ratio in the study area is the CO2 injection rate. Gas injection rate, injection sequence, and bottom hole pressure of production wells will all have an impact on the overall storage ratio. And alternate water and gas injection is quite significant. From the perspective of storage ratio, the optimal CO2 miscible flooding injection method has been optimized for the study area, which provides a reference for the implementation of CO2 underground storage technology in mines.

Thermodynamically favorable reactions shape the archaeal community affecting bacterial community assembly in oil reservoirs.

Microbial community assembly mechanisms are pivotal for understanding the ecological functions of microorganisms in biogeochemical cycling in Earth's ecosystems, yet rarely investigated in the context of deep terrestrial ecology. Here, the microbial communities in the production waters collected from water injection wells and oil production wells across eight oil reservoirs throughout northern China were determined and analyzed by proportional distribution analysis and null model analysis. A 'core' microbiota consisting of three bacterial genera, including Arcobacter, Pseudomonas and Acinetobacter, and eight archaeal genera, including Archaeoglobus, Methanobacterium, Methanothermobacter, unclassified Methanobacteriaceae, Methanomethylovorans, Methanoculleus, Methanosaeta and Methanolinea, was found to be present in all production water samples. Canonical correlation analysis reflected that the core archaea were significantly influenced by temperature and reservoir depth, while the core bacteria were affected by the combined impact of the core archaea and environmental factors. Thermodynamic calculations indicate that bioenergetic constraints are the driving force that governs the enrichment of two core archaeal guilds, aceticlastic methanogens versus hydrogenotrophic methanogens, in low- and high-temperature oil reservoirs, respectively. Collectively, our study indicates that microbial community structures in wells of oil reservoirs are structured by the thermodynamic window of opportunity, through which the core archaeal communities are accommodated directly followed by the deterministic recruiting of core bacterial genera, and then the stochastic selection of some other microbial members from local environments. Our study enhances the understanding of the microbial assembly mechanism in deep terrestrial habitats. Meanwhile, our findings will support the development of functional microbiota used for bioremediation and bioaugmentation in microbial enhanced oil recovery.

Tubing Failure in HP/HT Wells Investigated and Mitigated

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 20136, “Research and Application of Fracture Failure Control Technology for 13Cr Tubing in HP/HT Gas Wells,” by Lei Ma, Hongtao Liu, and Hailong Geng, PetroChina, et al., prepared for the 2020 International Petroleum Technology Conference, Dhahran, Saudi Arabia, 13-15 January. The paper has not been peer reviewed. Copyright 2020 International Petroleum Technology Conference. Reproduced by permission. Super 13Cr-110 tubing used in high-pressure, high-temperature (HP/HT) gas wells in the Tarim oil field has experienced numerous failures. After a series of investigations for root-cause analysis, the conclusion was that fracture of the tubing mechanistically is categorized as stress corrosion cracking (SCC) and is closely related to the application of phosphate-based completion fluid. Further tests indicated that Super 13Cr (S13Cr) tubing specimens experienced SCC with phosphate-based completion fluids contaminated with mud and oxygen, whereas formate-based completion fluid is compatible with S13Cr tubing. At present, 55 HP/HT gas wells in the field have used formate-based completion fluid with no tubing string fracture. Introduction Compared with the Gulf of Mexico, the North Sea, the South China Sea, the Qiongqiong Basin, and various Chinese oil and gas fields, the oil pipelines in the Tarim field are among the most difficult with regard to service conditions, which are characterized by extreme operating conditions such as high pump pressure and large displacement reform. Construction and high-yield alternating loads on tubing string and joint and a harsh, corrosive environment [chloride content greater than 80 000 mg/L, carbon dioxide (CO2) partial pressure greater than 1 MPa, and the presence of fresh and residual acid] pose significant challenges to the safe service of the tubing string. In the early stages of production, S13Cr oil pipe was selected as the completion string of the HP/HT gas well in the Kuqa mountain front, but in recent years, the S13Cr-110 pipe of the HP/HT gas well in the Tarim field has been continuously fractured. Failure accidents have caused serious economic losses. In the complete paper, through lateral comparison analysis of the failed tubing and indoor simulation experiments, the cause of the tubing fracture is discovered, solution measures are initiated, and good application results are achieved. Comparative Analysis of Oil-Pipe Failures Comparative Analysis of Fracture Macroscopic Morphology. When comparing the macroscopic topographic maps of oil-pipe failures seen in six studied wells, fracture locations of three wells are located in the coupling, while fracture locations of the other three wells are located on the body. In five wells (Wells A through E), the tubing fracture is neat, indicating brittle fracture with no plastic deformation. Well F, however, has a visible longitudinal crack on the surface of the tubing, and many burrs are visible at the fracture. Comparison and Analysis of Working Conditions of Failed Tubing. Through comparative analysis, it was determined that five of the six wells have under-gone acidification. The service shaft temperature, pressure, CO2 content, and formation water salinity of the failed wells differ, but, in five of the six wells, the tubing was exposed to a phosphate-based completion fluid.

Research on Fine Reservoir Distribution Based on Computer Modeling of Horizontal Well Data

With the decrease of oil reserves, the difficulty of oil reservoir exploitation is increasing day by day. In this case, the development of oil reservoir research is becoming more and more important. There are more complicated continental reservoirs in Chinese oilfields, which makes the development of oilfields more difficult. The traditional way of describing oilfields can no longer accurately describe the complex petroleum geological environment. Therefore, a more efficient and detailed description of modern reservoirs is needed. Methods, using computer modeling to analyze horizontal wells and quantitatively study the reservoir conditions.

Effect of microwave radiation time on the composition of gas produced from microwave assisted heavy oil upgrading

One of the major challenges of microwave assisted EOR is the composition of produced liquid and gases. In this present contribution, heavy oil from one of Chinese oil field were exposed to time variant microwave radiation until 18 min. Experimental results illustrates that rapid increase in the gas flow rate was observed between 3 and 12 min radiation and after 12 min radiation, this value tends to be constant. Moreover, experimental observations shows that mole% of C1-C4 increase with radiation time; however, mole% of heavier components decreases. The mole% of methane and ethane has a rapid increase from 9 to 12 min radiation.

Machine-Learning-Based Early-Warning System Maintains Stable Production

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 197365, “A Novel Early Warning System of Oil Production Based on Machine Learning,” by Kang Ma, Hanqiao Jiang, and Junjian Li, China University of Petroleum-Beijing, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed. After long-term waterflooding, a number of mature oil fields in China have entered the high-water-cut stage, and abnormal production decline has become the primary problem for stable production. This paper describes an accurate, three-step, machine-learning-based early warning system (EWS) that has been used to monitor production and guide strategy in the Shengli field. Adding artificial samples to the training process improved the system’s prediction accuracy greatly (Fig. 1). Introduction For conventional Chinese oil fields that have entered the high-water-cut stage after decades of waterflooding, stabilizing production has become increasingly difficult. After stimulation treatments throughout the field’s history, abnormal decline rates—that is, exceeding 5%—occurred more frequently. Production declined dramatically in 2004 and has not been maintained since then. To prevent future abnormal production declines, an effective EWS was needed that could release a production alarm to enable engineers to take preventive measures in advance. The complete paper includes a discussion of various early-warning models and their limitations. The paper discusses an EWS based on a neural network method using a previously established data set. Factors that can affect the abnormal decline were selected. The index sets of production composition and injected and produced water obtained from practical statistics were considered as the main assessment indicators. Grey relational analysis was used to evaluate the importance of the different indicators and to eliminate redundant parameters. Machine learning was adopted to build the EWS. Using the degree of deviation from normal as the input data for the prediction model provided the highest accuracy. However, the basic machine-learning model contains many input parameters that cannot be obtained easily. The number of input parameters was optimized on the basis of the variation of accuracy under different input parameter numbers. To improve prediction accuracy, artificial samples were added to the training process. The prediction accuracy of the final optimization model can reach 92%. The result reveals the possibility of the occurrence of anomalous decline in different reservoirs, which can guide oilfield production strategy effectively. The EWS was verified by oilfield production. Fundamentals of Neural-Network Classification Neural networks use existing data to determine the implicit model between input and output data. Classification and prediction are the most common applications. Neural networks are used in the industry to solve classification and regression problems.

Optimization of waterflooding performance by using finite volume-based flow diagnostics simulation

From a visual point of view, volumetric information about reservoir portioning and communication such as sweep, flow patterns, and drainage zones are longer better interpreted and pictured when presented by an average volumetric flux calculation. To this hand, finite volume discretization can be used to substitute streamline simulation-based finite difference to assess flow diagnostic information. Herein, we use finite volume-based flow diagnostics to optimize waterflooding. In particular, we discretize in finite volume the flow equation from Darcy’s law single-phase incompressible flow steady state combining with two auxiliary flow equations, time of flight and stationary tracers using the two-point flux approximation to describe fluid particles motion and flow lines. In addition, with the estimation of dynamic heterogeneity, we compute the Lorenz coefficient to highlight the reservoir flow and storage capacity characterization. To optimize waterflooding rates, we first, use an objective function the equalized Lorenz coefficient got through the evaluation of average travel time in cells to increase sweep efficiency and decrease the dynamic heterogeneity coefficient. Second, following the same target, we use the flow diagnostic interactive tools to study the volumetric sweep displacement front and harmonize the flooding breakthrough. In this work, our conceptual approach is to see the reservoir initially filled with oil; then, optimizing the Lorenz coefficient leads us to an oil recovery improvement. To be pragmatic, we apply our waterflooding performance optimization model on two case studies, the ninth SPE comparative solution project, a reexamination of black-oil (synthetic case) and ZHNBA Chinese oilfield (real field dataset).

Predictive model for water absorption in sublayers using a machine learning method

The sedimentary rhythm of Chinese oilfields is complicated and the heterogeneity is extremely strong. Allocating water absorption of each sublayer by dividing coefficient or numeric simulation cannot accurately reflect the actual water injection of the reservoir. Calculation based on water absorption profile monitored on site is the most commonly used method in oil field. However, access to these type of data is limited due to its cost and time related to acquisition. In this study, a machine learning approach was adopted to predict water absorption in sublayer based on geologic and production parameters of injectors and producers. On the one hand, it can save test costs. On the other hand, it can continuously predict water absorption of sublayers, and make up for water injection wells with insufficient injection profiles. A handful of training observations are obtained from on-site monitoring. Interwell connectivity is first conducted to identify connected producers for injectors. Introducing interwell connectivity helps to constitute predictor variables and yield significant improvements in feature selection. Connectivities in the well group are represented by similarity between injection sequence and production sequence, which is computed by Dynamic Time Warping. Average importance of predictors are then measured based on Mean Decrease Impurity, Mean Decrease Accuracy, and Ridge regression. Some relative important features are selected to consist the final predictors. The Extreme Gradient Boosting model is developed and then trained for making predictions given any set of observations. The proposed approach is validated by using actual field case from SL oilfield, China. Results show a significant correlation between predictions and actual value from on-site monitoring.

CCUS evaluation and simulation in a Chinese oil field

CCUS evaluation and simulation in a Chinese oil field

CCUS evaluation and simulation in a Chinese oil field

This paper studies the evaluation issue of a preparatory CCUS in China. Economic and environmental evaluation are presented with the delicate simulations. The analysis of the evaluation results show that the CCUS project will be feasible if the oil price is high enough or the storage allowance can be given. The results will be useful for the future CCUS projects and will be useful for the government decisions on CCUS.

Treatment Technology on Polymer Flooding Wastewater in Henan Oilfield

Polymer flooding has became one of the most important oil recovery technologies with Chinese oilfields coming into tertiary recovery, which lays a solid foundation for high and stable yields in oilfields. But with it’s large-scale industrial production, polymer flooding technology also brings difficulties for the disposal and treatment of polymer flooding wastewater. Compared with conventional water flooding wastewater technology, polymer flooding wastewater not only contains oil but also lots of polymer. “Old three sets” process cannot meet the national discharge standard or the injection water quality standard. Relied on HeNan oilfield united station, this paper studied on the treatment of polymer flooding wastewater, a kind of efficient flocculant was selected for the treatment of polymer flooding wastewater and a set of reasonable technological process was recommended, making the wastewater after disposal meet the injection water quality standard.