bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study and Application of Pulse Hydraulic Fracturing for Tight Oil Reservoir
Authors: Xu Hongxing , Sun Hu , Wang Zuwen , Zhang Mian , Lan Jianping , Deng Binqi , Li Yanhong 
Published: March 2021

Abstract
Pulse hydraulic fracturing is a promising stimulation technology to enhance the effectively permeability of coal seams. The fundamental of pulse hydraulic fracturing is that fracturing fluids with a certain frequency are injected into coal, resulting in the rupture of coal and forming a well-distributed fracture network due to the pulse loading. Better effects of gas extraction using pulse hydraulic fracturing had been gotten compared with that of hydraulic fracturing. Accordingly, how to apply pulse hydraulic fracturing technology to improve the fracturing effect of tight and shale reservoirs is a question worth thinking about, although this is very challenging due to the totally different downhole operating conditions. In this paper, experimental apparatus for fatigue damage of quasi-triaxial rock under alternating loads was established. The maximum injection pressure is 50MPa, while the pulse pressure amplitude is greater than 5MPa, and the pulse frequency is adjustable from 0 to 50Hz. Rock failure experiments under pulsating load were carried out and the effects of different hydraulic pulse parameters and rock properties on rock damage were studied. Experimental results show that hydraulic pulse has different effects on rock compressive strength and fracture pressure of different properties. With the increase of hydraulic pulse frequency, the influence on rock compressive strength increases firstly and then decreases. With the increase of pulse pressure amplitude, the influence on rock strength increases. With the increase of hydraulic pulse processing time, the influence on rock fracture pressure increases firstly and then tends to stabilize. Hydraulic pulse has the greatest influence on the compressive strength and fracture pressure of He 8 reservoir, followed by Chang 8 and Chang 6 reservoir of Changqing Oilfield in China. Based on the experimental results, hydraulic pulse frequency is preferred to be about 18-20Hz, accordingly, a downhole hydraulic pulse generator is designed and manufactured. The indoor test results show that the generator performance meets the design requirements. Field tests of pulse hydraulic fracturing were carried out in 3 wells in Changqing tight oil reservoir. Encouraging results were obtained, the average construction pressure was reduced obviously and average daily production per well increased significantly compared to adjacent wells.
Experimental Investigation of CO2 Huff and Puff Process Based on Three-Dimensional Physical Models
Authors: Tang Xiang , Li Yiqiang , Yu Zhihao , Xu Miaomiao , Zhou Rui 
Published: March 2021

Abstract
As an important enhanced oil recovery method for tight reservoirs, CO2 huff and puff (HnP) is getting more attention in recent years. It is urgent to systematically study the characters of CO2 HnP. Due to the limitations of numerical simulation, it is more reliable and reasonable to study the development characteristics of CO2 HnP through experiments. The objective of this work is to conduct comprehensive experiments to clarify the characters and main mechanisms of CO2 HnP process based on the three-dimensional (3D) physical models.
A 3D physical experimental apparatus with circumstance of high temperature and high pressure has been developed, which is mainly used to support the models with a fixed confining pressure and temperature. Based on the similarity criterion of dimensionless conductivity, two different 3D physical models (30cm×30cm×3.5cm) with a horizontal well and fractures are made from outcrops to imitate the different reservoirs. Under these preconditions, some CO2 HnP experiments were conducted to investigate the development characteristics from the 3D physical models.Also,long core experiments were carried out to establish and verify the production prediction model, combined with expansion test, diffusion test and nuclear magnetic resonance test.
The experimental results show that the development process of CO2 HnP can be divided into four stages: CO2 backflow, Gas production with attached oil, High-speed oil production and Decay. The main mechanism of oil production in each stage is different. With the increase of the cycle number, the recovery factors of both models first increase and then decrease, while the oil/gas replacement rates may drop rapidly. The fractures have been proven to increase the oil recovery from 21.2% to 36.7% after ten rounds of CO2 HnP. Based on the analysis of expansion and molecular diffusion, a production prediction model was established, and the average error between the predicted results and the experimental results is 7.7%, which has good applicability and accuracy.
In this paper, some large-scale 3D physical model experiments with CO2 HnP for tight reservoir were elaborated. The development characteristics of CO2 HnP were analyzed, and a production prediction model was established. A lot of valuable experimental data and a better understanding on CO2 HnP process in tight reservoir have been obtained.
Inversion of Reservoir Parameters by Ultra-High Temperature and High Pressure CO2 Gas Reservoirs with Nuclear Logging
Authors: Zhang Hengrong , Xiao Lizhi , Wu Wensheng , Fu Xinyue , He Shenglin 
Published: March 2021

Abstract
The Yinggehai basin is located in the western part of the South China Sea, the burial depth of the Huangliu and Meishan formations in the target layer is close to 4000 meters, the formation temperature is close to 200 degrees Celsius, and the formation pressure is up to 100 MPa. The reservoir is characterized by low porosity-ultra-low permeability, heavy carbonate cement, complex CO2 content, this leads to complex neutron and density logging effects.
The solubility of CO2 Above CH4, the solubility change with temperature and pressure is different from CH4, which makes it difficult to identify the CO2 gas layer. In this paper, based on the difference in the physical characteristics of CO2 and CH4, the Boltzmann equation combined with MCNP software was used to simulate the neutron and density logging responses under different CO2 saturations. Environmental factors such as temperature and pressure, carbonate cement, mud content and pores were studied To measure the effect of logging response, the LM inversion method is used to jointly invert CO2 saturation of density and neutron logs.
The purpose of the inversion is to reduce the non-uniqueness of the evaluation of porosity and CO2 saturation. By introducing the Levenberg-Marquardt (LM) method, the neutron logging response equation of the porosity, argillaceous content, CO2, CH4 in the rock and the corresponding temperature and pressure is solved, and also the response equation of above parameters to density logging, where porosity and CO2 content are the key parameters, and the calculation results prove the effectiveness of the method by comparing the sampling data. The results show that the accuracy of the estimated CO2 saturation is increased by 10% compared with the conventional interpretation method, and the new simulation method improves the calculation speed several times compared to the MCNP software.
The joint inversion method has been successfully applied to field data, which has greatly improved the saturation evaluation results of traditional logging interpretation methods, can be extended to other fields of nuclear logging simulation and inversion.
Enzymatic Treatment of Phenolic Wastewater: Effects of Salinity and Biosurfactant Addition
Authors: Onaizi Sagheer A. 
Published: March 2021

Abstract
Water contaminated with phenols is produced from several oil and gas related industries. Although there are a number of treatment methods, enzymatic wastewater treatment is more attractive due to its sustainability, environmental-friendliness, and mild nature. A key limitation of this process, however, is the enzymatic deactivation (whether complete or partial) during the treatment process. This limitation might be addressed to a certain extent through the addition of biosurfactants to the reaction medium. Thus, the key aim of this study is to utilize laccase (an oxidoreductase enzyme from Trametes versicolor) to remove bisphenol A (BPA) from wastewaters in the presence of rhamnolipid biosurfactant. Since most wastewaters contain inorganic salts, the efficacy of enzymatic treatment of high saline wastewaters has been evaluated. The beneficial effect of the biosurfactant addition during the enzymatic treatment of highly saline phenolic wastewater has been also assessed. Additionally, the effect of increasing the biocatalyst and the phenolic pollutant concentrations have been also probed. The results showed that the BPA degradation rate increases with increasing the enzyme concentration. The extent of BPA removal also increased with increasing the biocatalyst concentration, approaching almost a complete removal at an enzyme concentration of 400 ppm. The BPA degradation rate also increased almost linearly with increasing its initial concentration; however, its removal extent showed the opposite trend. The addition of as low as 1 ppm rhamnolipid biosurfactant to the reaction medium increased both the BPA degradation rate and the removal extent relative to the biosurfactant-free wastewater samples. The addition of the biosurfactant to the reaction medium boosted the BPA degradation rate and the removal extent by 1.1- to 1.23-fold. The highest BPA degradation rate and removal enhancement (about 23% higher than those in the absence of the biosurfactant) was obtained for BPA-rhamnolipid mass ratio of 50:1. The presence of salt severely reduced the BPA degradation rate and removal. The addition of 20 mM NaCl resulted in about 1.7-fold drop in the BPA degradation rate and removal. The drop in the BPA degradation rate and removal reached more than 3.6-fold at 500 mM NaCl. The addition of 1 ppm rhamnolipid partially compensated the negative effect of salinity, providing relatively higher BPA degradation rate and removal at all examined salinity levels. The findings reported herein reveal the positive effect of biosurfactant addition to the enzymatic reaction medium and the need for the salt removal prior to subjecting the saline wastewaters to enzymatic treatment.
An Artificial Intelligence Neural Networks Driven Approach to Forecast Production in Unconventional Reservoirs – Comparative Analysis with Decline Curve
Authors: Khan Hassan , Louis Clifford 
Published: March 2021

Abstract
Subsurface engineers pivot on surveillance of reservoir performance for future production rates and plan the optimization strategies at earliest. There are some techniques preferred for unconventional reservoirs such as numerical simulation and decline curve analysis (DCA) for production forecasting, but the uncertainty of uneconomical well test data often occurs in unconventional resources. Moreover, reservoir engineers can also hit a tailback in optimizing and tuning the model. Further, for DCA this approach is only appropriate for well/reservoir that are under boundary dominant flow regime, whereas fracture dominant flow regime is often observed for a longer period in unconventional hydraulically fractured reservoirs. Therefore, to resolve this issue, oil & gas industry (O&G) can adopt AI (Artificial Intelligence) based Algorithms for production forecasting.
This paper presents a data-driven algorithm, known as Artificial Neural Networks (ANNs), along with time series forecasting that is a well-known statistical technique. Machine learning model trained by a past well performance data such as tubing head pressure (THP), flowing bottom-hole pressure can predict future production rates. This can be an efficient technique for subsurface engineers to monitor and optimize well performance. Time series neural networks were used for training the model at top and bottom node of the well with variating pressures in the past. After training and validation, the model predicted a target parameter that was gas rate. ANNs are inspired by biological neurons that are present in human brain, a powerful computing tool to make decisions after fueling itself with data. Moreover, prediction (t+1) nonlinear automated regression is preferred for accurate step ahead.
Production rates and constraints of unconventional reservoirs were used to train the model. In our results, the NN based model gave the co-efficient of determination (R2) of 0.996 that shows nearly an exact precision. Furthermore, the values generated from NN Model and Arp's decline curve calculations were plotted for validation and it turned out that ANN can accurately predict the parameters.
The Neural Network model is a novel approach for production forecasting, of unconventional reservoirs and help engineers in corporate decision making. This approach can mitigate the need of uneconomical well test operations and further provide confidence to production engineers in terms of data and result expectations.
Development of New Geopolymer-Based System for Challenging Well Conditions
Authors: Abd Rahman Siti Humairah , Medvedev Anatoly , Yakovlev Andrey , Sazali Yon Azwa , Jain Bipin , Hassan Norhasliza , Thompson Cameron 
Published: March 2021

Abstract
With the development of new oil formations and with the advent of new directions in the global energy sector, new requirements for materials for well construction appear. With the close attention to environmental footprint and unique properties, one of the promising materials for well cementing is geopolymers. Being a relatively new material, they are characterized by low carbon footprint, high acid resistance and attractive mechanical properties. This article is aimed at developing new geopolymer slurries for the oil industry, their characterization and field implementation analysis.
With the ultimate goal of developing a methodology for the analysis of raw materials and designing the geopolymer slurries, studies were carried out on various raw materials, including different types of fly ash. Based on the data obtained and rapid screening methods, an approach was developed to formulate a geopolymer composition recipe. Since not all cement additives directly work in geopolymers, special attention was paid to control the thickening time and fluid loss. The methods of XRD, XRF, ICP-MS, density, particle size distribution measurements as well as API methods of cement testing were used to understand the composition and structure of the materials obtained, their properties and design limitations. A special approach was applied to study the acid resistance of the materials obtained and to compare with conventional cements and slags.
Using one of the most common sources of aluminosilicate, fly ash, formulations with a density of 13.5 – 16.5 lbm/galUS were tested. A sensitivity analysis showed that the type of activator and its composition play a critical role both in the mechanical properties of the final product and in the solidification time and rheological properties of the product. The use of several samples of fly ash, significantly different in composition, made it possible to formulate the basic rules for the design of geopolymers for the oil industry. An analysis was also carried out on 10 different agents for filtration and 7 moderators to find a working formulation for the temperature range up to 100°C. The samples were systematically examined for changes in composition, strength, and acid resistance was previously measured.
Despite the emergence of examples of the use of geopolymers in the construction industry and examples of laboratory testing of geopolymers for the oil industry, to the best of our knowledge, there has been no evidence of pumping geopolymers into a well. Our work is an attempt to develop an adaptation of the construction industry knowledge to the unique high pressure, high temperature conditions of the oil and gas industry. The ambitions of this work go far beyond the laboratory tests and involve yard test experiments.
Application of Multi-Information Fusion Fracture Modelling Based on Neural Network in Carbonate Dual: Medium Reservoir
Authors: Dan Lingling , Shi Changlin , Wen Jiatao , Hu Yunting , Wei Li , Li Yunxiu , Zhang Jian , Tian Panpan 
Published: March 2021

Abstract
G oilfield in China is a dual medium oilfield dominated by carbonate reservoir, where faults and fractures are well developed. After G oilfield was put into production, water cut has rised quickly and oil production decreased rapidly. The heterogeneity of fracture development is very strong. Because of unclear understanding of the fracture distribution, oilfield development plan can not accurately identify risks and potentials accurately. As a result, production goals are not as expected. Due to limited core data and imaging logging data, conventional methods cannot characterize fracture information in this oilfield accurately and comprehensively. In order to solve the problems mentioned above, this paper makes full use of logging, geology, seismic and other data to carry out fine characterization of fracture with multi-information fusion fracture modeling based on neural network. Firstly, neural network technology is used to predict the fracture density curves of wells with conventional logging data. Secondly, combined the nonlinear fusion of multiple pre-stack seismic attributes including Tectonic Stress Field, P-Wave AVO and Seismic Discontinuity Detection based on neural network, the fracture density probability model is created to predict three-dimensional distribution of fractures. Thirdly, the fracture Intensity model is set up under the constraint of strict variation function analysis and fracture density probability model. Finally, fracture parameters are obtained according to core observation and imaging logging data, and the fracture network is established by geostatistics modeling method and coarsened equivalent into fracture property model. Blind well validation and dynamic validation are used to verify the validity of fracture model. Under the guidance of research results in this paper, the oilfield development plan is optimized and adjusted. It has also been proved that fracture characteristic of new wells is consistent with pre-drilling prediction result. Most areas with relatively high fracture development degree are corresponding to well groups with faster water cut rising rate. Against the backdrop of global oil economic downturn, fracture modeling technology studied in this paper provides strong technical guidance for oilfield development plan, therefore reducing oilfield development risks and maximizing economic benefits.
Digital Transformation of Production Allocation and Management for Oil and Gas Field
Authors: Zhao Han , Chen Guo , Zhang Su , Wang Fu Yong , Zhang Hua Yi , Li Meng , Xu Wen Qi , Wu Ting , Ren Jing Si 
Published: March 2021

Abstract
Output is the core index of oil and gas enterprises, and the management of output is very important to the operation of oil and gas enterprises. Each year, oil and gas companies will develop a production allocation plan according to well productivity. Under the guidance of the allocation plan, the annual production target would finally be achieved.
In the digital transforming stage of development and production management of oil and gas fields, the traditional offline manual production allocation can no longer meet the needs of delicacy management. For this reason, based on the development and production management platform, a system of production allocation and management was established and deployed in an international oil and gas company.
In this paper, through the information platform concept model, introduced the development and production management platform construction method. Based on the platform architecture, taking production allocation and management as an example, the system construction process is introduced from the aspects of business process splitting, system function design, data and software integration call, etc. Furthermore, through the integration of intelligent oil and gas project results based on the platform architecture, the integrated application of intelligent workflow to core business activities in the production allocation and management process is realized.
The platform has been fully applied by a large natural gas production company. Through this platform, a management mode with "business standardization, process standardization, and execution standardization" was adopted to realize the comprehensive transformation of production allocation and management from the traditional mode to the digital one. The application of the platform shows that: the process of production allocation and management optimized in process-driven way is traceable and can form a business closed loop; through process reorganization and integrated application, the efficient business collaboration across specialities and departments has been achieved, improving the scientific decision-making ability and the level of business management;
Assurance and Verification of Safety Critical Elements in Asset Management
Authors: Kamal Girish 
Published: March 2021

Abstract
Safety Critical Elements (SCEs) are the equipment and systems that provide the foundation of risk management associated with Major Accident Hazards (MAHs). A SCE is classified as an equipment, structure or system whose failure could cause or contribute to a major accident, or the purpose of which is to prevent or limit the effect of a major accident.
Once the SCE has been ascertained, it is essential to describe its critical function in terms of a Performance Standard. Based on the Performance Standard, assurance tasks can be stated in the maintenance system to ensure that the required performance is confirmed. By analyzing the data in the maintenance system, confidence can be gained that all the SCEs required to manage Major Accidents and Environmental Hazards are functioning correctly. Alternatively, corrective actions can be taken to reinstate the integrity of the systems if shortcomings are identified.
This paper shall detail out how the MAH and SCE Management process is initiated to follow the best industry practices in the identification and integrity management of major accident hazards as well as safety critical equipment. The tutorial shall describe in detail the following important stages:Identification of Major Accident HazardsIdentification of Safety Critical Equipment, involved in managing Major Accident HazardsDefine Performance Standards for these Safety Critical EquipmentExecution of the Assurance processes that maintain or ensure the continued suitability of the SCE Equipment, and that these are meeting the Performance StandardsVerification that all stages have been undertaken, any deviations being managed and thus that Major Accident Hazards are being controlled.Analyze and Improve
Through the diligent application of these stages, it is possible to meet the requirements for MAH and SCE Management process giving a better understanding and control of risks in the industry.