A new production component method for natural gas development planning

Based on an analysis of the limitations of conventional production component methods for natural gas development planning,this study proposes a new one that uses life cycle models for the trend fitting and prediction of production.In this new method,the annual production of old and new wells is predicted by year first and then is summed up to yield the production for the planning period.It shows that the changes in the production of old wells in old blocks can be fitted and predicted using the vapor pressure model(VPM),with precision of 80%e95%,which is 6.6%e13.2%higher than that of other life cycle models.Furthermore,a new production prediction process and method for new wells have been established based on this life cycle model to predict the production of medium-to-shallow gas reservoirs in western Sichuan Basin,with predication error of production rate in 2021 and 2022 being 6%and 3%respectively.The new method can be used to guide the medium-and long-term planning or annual scheme preparation for gas development.It is also applicable to planning for large single gas blocks that require continuous infill drilling and adjustment to improve gas recovery.

Effects of Milling Methods on Rice Flour Properties and Rice Product Quality:A Review

High-quality rice flour is the foundation for the production of various rice-based products.Milling is an essential step in obtaining rice flour,during which significant changes occur in the physicochemical and quality characteristics of the flour.Although rice flour obtained through mainstream wet milling methods exhibits superior quality,low production efficiency and wastewater discharge limit the development of the industry.Dry milling,on the other hand,conserves water resources,but adversely affects flour performance due to excessive heat generation.As an emerging powder-making technique,semi-dry milling offers a promising solution by enhancing flour quality and reducing environmental impact.This is achieved by minimizing soaking time through hot air treatment while reducing mechanical energy consumption to reach saturated water absorption levels.However,continuous production remains a challenge.This comprehensive review summarizes the effects of various milling technologies on rice flour properties and product qualities.It also discusses key control indicators and technical considerations for rice flour processing equipment and processes.

Evaluation of the Shallow Gas Hydrate Production Based on the Radial Drilling-Heat Injection-Back Fill Method

It has been evidenced that shallow gas hydrate resources are abundant in deep oceans worldwide.Their geological back-ground,occurrence,and other characteristics differ significantly from deep-seated hydrates.Because of the high risk of well construction and low production efficiency,they are difficult to be recovered by using conventional oil production methods.As a result,this paper proposes an alternative design based on a combination of radial drilling,heat injection,and backfilling methods.Multi-branch holes are used to penetrate shallow gas hydrate reservoirs to expand the depressurization area,and heat injection is utilized as a supplement to improve gas production.Geotechnical information collected from an investigation site close to the offshore production well in the South China Sea is used to assess the essential components of this plan,including well construction stability and gas production behavior.It demonstrates that the hydraulic fracturing of the 60mbsf overburden layer can be prevented by regulating the drilling fluid densities.However,the traditional well structure is unstable,and the suction anchor is advised for better mechanical performance.The gas produc-tion rate can be significantly increased by combining hot water injection and depressurization methods.Additionally,the suitable produc-tion equipment already in use is discussed.

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

Monte Carlo Method for the Uncertainty Evaluation of Spatial Straightness Error Based on New Generation Geometrical Product Specification

Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.

The coalbed methane production potential method for optimization of wells location selection

A gas production potential method for optimization of gas wellsite locations selection is proposed in terms of the coalbed gas resources volume and the recoverability. The method uses the actual data about reservoirs in a coalbed gas field in central China to optimize wellsite locations in the studied area in combination with the dynamic data about actual production in the coalbed gas field, selects a favorable subarea for gas wells deployment. The method is established based on the basic properties of coal reservoirs, in combination with the coalbed thickness and the gas content to make an analysis of the gas storage potential of a coal reservoir, as well as resources volume and the permeability of a coal reservoir. This method can be popularized for optimization of wellsite locations in other methane gas development areas or blocks.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.

Design Theory and Method of Complex Products:A Review

Design is a high-level and complex thinking activity of human beings,using existing knowledge and technology to solve problems and create new things.With the rise and development of intelligent manufacturing,design has increasingly reflected its importance in the product life cycle.Firstly,the concept and connotation of complex product design is expounded systematically,and the different types of design are discussed.The four schools of design theory are introduced,including universal design,axiomatic design,TRIZ and general design.Then the research status of complex product design is analyzed,such as innovative design,digital design,modular design,reliability optimization design,etc.Finally,three key scientific issues worthy of research in the future are indicated,and five research trends of“newer,better,smarter,faster,and greener”are summarized,aiming to provide references for the equipment design and manufacturing industry.

Evaluation methods of man-machine-environment system for clean and safe production in phosphorus mines: A case study

Phosphorus is an essential element in agricultural production and chemical industry. However, since the risk of casualties and economic loss by mining accidents, the application of clean and safe production in phosphorus mines encounters great challenges. For this purpose, a man-machine-environment system composed of evaluation indexes was established, and the grading standards of indexes were defined. Firstly, the measurements of 39 qualitative indexes were obtained through the survey data. According to the measured values of 31 quantitative indexes, the measurements of quantitative indexes were calculated by linear measurement function(LM) and other three functions. Then the singleindex measurement evaluation matrixes were established. Secondly, the entropy weight method was used to determine the weights of each index directly. The analytic hierarchy process(AHP) was also applied to calculate the weights of index and index factor hierarchies after the established hierarchical model. The weights of system hierarchies were given by the grid-based fuzzy Borda method(GFB). The comprehensive weights were determined by the combination method of AHP and GFB(CAG). Furthermore, the multi-index comprehensive measurement evaluation vectors were obtained.Thirdly, the vectors were evaluated by the credible degree recognition(CDR) and the maximum membership(TMM)criteria. Based on the above functions, methods, and criteria, 16 combination evaluation methods were recommended.Finally, the clean and safe production grade of Kaiyang phosphate mine in China was evaluated. The results show that the LM-CAG-CDR is the most reasonable method, which can not only determine the clean and safe production grade of phosphorus mines, but also improve the development level of clean and safe mining of phosphorus mines for guidance.In addition, some beneficial suggestions and measures were also proposed to advance the clean and safe production grade of Kaiyang phosphorus mine.

Rapid diagnostic method for transplutonium isotope production in high flux reactors

Transplutonium isotopes are scarce and need to be produced by irradiation in high flux reactors.However,their production is inefficient,and optimization studies are necessary.This study analyzes the physical nature of transplutonium isotope produc-tion using ^(252)Cf,^(244)Cm,^(242)Cm,and ^(238)Pu as examples.Traditional methods based on the Monte Carlo burnup calculation have the limitations of many calculations and cannot analyze the individual energy intervals in detail;thus,they cannot sup-port the refined evaluation,screening,and optimization of the irradiation schemes.After understanding the physical nature and simplifying the complexity of the production process,we propose a rapid diagnostic method for evaluating radiation schemes based on the concepts“single energy interval value(SEIV)”and“energy spectrum total value(ESTV)”.The rapid diagnostic method not only avoids tedious burnup calculations,but also provides a direction for optimization.The optimal irradiation schemes for producing ^(252)Cf,^(244)Cm,^(242)Cm,and ^(238)Pu are determined based on a rapid diagnostic method.Optimal irradiation schemes can significantly improve production efficiency.Compared with the initial scheme,the optimal scheme improved the production efficiency of ^(238)Pu by 7.41 times;^(242)Cm,11.98 times;^(244)Cm,65.20 times;and ^(252)Cf,15.08 times.Thus,a refined analysis of transplutonium isotope production is conducted and provides a theoretical basis for improving production efficiency.

Uncertain Multidisciplinary Design Optimization on Next Generation Subsea Production System by Using Surrogate Model and Interval Method

The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.

COMPLEX INNER PRODUCT AVERAGING METHOD FOR CALCULATING NORMAL FORM OF ODE

This paper puts forward a complex inner product averaging method for calculating normal form of ODE. Compared with conventional averaging method, the theoretic analytical process has such simple forms as to realize computer program easily. Results can be applied in both autonomous and non-autonomous systems. At last, an example is resolved to verify the method.

An improved data space inversion method to predict reservoir state fields via observed production data

A data-space inversion(DSI)method has been recently proposed and successfully applied to the history matching and production prediction of reservoirs.Based on Bayesian theory,DSI can directly and effectively obtain good posterior flow predictions without inversion of geological parameters of reservoir model.This paper presents an improved DSI method to fast predict reservoir state fields(e.g.saturation and pressure profiles)via observed production data.Firstly,a large number of production curves and state data are generated by reservoir model simulation to expand the data space of original DSI.Then,efficient history matching only on the observed production data is carried out via the original DSI to obtain related parameters which reflects the weight of the real reservoir model relative to prior reservoir models.Finally,those parameters are used to predict the oil saturation and pressure profiles of the real reservoir model by combining large amounts of state data of prior reservoir models.Two examples including conventional heterogeneous and unconventional fractured reservoir are implemented to test the performances of predicting saturation and pressure profiles of this improved DSI method.Besides,this method is also tested in a real field and the obtained results show the high computational efficiency and high accuracy of the practical application of this method.

Development of an analytical method for multi-residue quantification of 18 anthelmintics in various animal-based food products using liquid chromatography-tandem mass spectrometry

In this study,we developed a simple screening procedure for the determination of 18 anthelmintics(including benzimidazoles,macrocyclic lactones,salicylanilides,substituted phenols,tetrahydropyrimidines,and imidazothiazoles)in five animal-derived food matrices(chicken muscle,pork,beef,milk,and egg)using liquid chromatography-tandem mass spectrometry.Analytes were extracted using acetonitrile/1% acetic acid(milk and egg)and acetonitrile/1% acetic acid with 0.5 mL of distilled water(chicken muscle,pork,and beef),and purified using saturated n-hexane/acetonitrile.A reversed-phase analytical column and a mobile phase consisting of(A)10 mM ammonium formate in distilled water and(B)methanol were used to achieve optimal chromatographic separation.Matrix-matched standard calibration curves(R^(2)≥0.9752)were obtained for concentration equivalent to ×1/2,×1,×2,×3,×4,and×5 fold the maximum residue limit(MRL)stipulated by the Korean Ministry of Food and Drug Safety.Recoveries of 61.2e118.4%,with relative standard deviations(RSDs)of ≤19.9%(intraday and interday),were obtained for each sample at three spiking concentrations(×1/2,×1,and ×2 the MRL values).Limits of detection,limits of quantification,and matrix effects were 0.02e5.5 mg/kg,0.06e10 mg/kg,and -98.8 to 13.9%(at 20 μg/kg),respectively.In five samples of each food matrix(chicken muscle,pork,beef,milk,and egg)purchased from large retailers in Seoul that were tested,none of the target analytes were detected.It has therefore been shown that this protocol is adaptable,accurate,and precise for the quantification of anthelmintic residues in foods of animal origin.

Flow-Based Iodometric Extraction Method by the Hollow Fiber Membrane for the Determination of Selenium Sulfide Levels in Cosmeceutical Products

A flow-based iodometric extraction method for the determination of selenium sulfide was developed and applied to cosmeceutical products. Iodine which was generated from the reduction of selenium（IV） ions by iodide ion was on-line extracted using a polypropylene HFM （hollow fiber membrane） liquid extraction technique. The HFM extraction unit was constructed and used to support an organic solvent （hexane） and separate between the organic phase and aqueous phase. The resulting purple extract was carried to a fiber optic spectrophotometric detector for the measurement at 521 nm. Parameters which affected the extraction efficiency, sensitivity and sample throughput such as iodide （selenium molar ratio, extraction time and washing time between the cycles） were investigated and optimized. A linear dynamic range of 80-373 mg.Lt selenium solution was obtained with an extraction time of 60 sec. The total analysis time including washing was about 180 sec which provided a sample throughput of approximately 20 samples＇hr1 and excluded the sample pre-treatment. The recoveries for the determination of selenium in the forms of selenium dioxide and selenium sulfide were in the range of 103%-104% with 1%-3% RSD （relative standard deviation）. The relative errors of this method which was applied for determination of selenium sulfide levels in an anti-dandruff shampoo and a cosmeceutical bead sample were both less than 2.5%.

Chemical Analysis Method for Carbon Bearing Refractory Products——Determination of the Total Carbon by Combustion Gravimetric Method

GB/T 13245-91 1 Theme and Scope This standard specifies the method abstract, reagents, apparatus, specimen, analyzing procedure, result calculation and permissible tolerance used for determination of the total carbon with combustion gravimetric method.

Evaluation of Lean Product Development Stages of Autonomous Vehicle Technologies with AHP Method

The potential innovation and emerging workforce created by autonomous vehicle technologies, which have just entered the lean product development disciplines, play an important role in the development or change of the automotive manufacturing industry. Therefore, the intensity of work and the innovation practices brought by the technologies in question at each step of very different and interdisciplinary studies deeply affect the new and lean product development steps. Comparatively measuring the operating weight of new autonomous vehicle technologies in different company structures in these lean product development steps has important consequences for the development and change of the automotive industry under heavy global competition. On the other hand, it is difficult to measure the innovation input or the use of new autonomous technology under the AHP mathematical model of each part that constitutes the whole of the lean product development process, but it also creates the future predictions of the sector. The Analytical Hierarchy Process (AHP), which is one of the multi-purpose decision-making methods, was used to determine the most intense value creation, the design and development phase where there is innovation input, or the lean product development discipline throughout the whole process. The AHP method was preferred for the comparative analysis and synthesis of different applications or similar approaches in the automotive manufacturing industry companies (global and local) and lean product development processes in the field study of the research, under qualitative data. Under the AHP mathematical model created in the research, it was aimed to measure interdisciplinary clusters with a focus on new technology and to identify similarities or differences under alternative applications created by different company structures and to compare them systematically and evaluate them mathematically. In the study, the AHP mathematical model was used to compare lean product development processes and the use of new autonomous vehicle technologies, and the Expert Choice program was preferred in the application of the method.

LOCAL SALINE APPROXIMATION METHODS FOR SINGULAR PRODUCT INTEGRATION

The purpose of this paper is to propose and study local spline approximation methods for singular product integration,for which;i)the precision degree is the highest possible using splint approximation; ii) the nodes fan be assumed equal to arbitrary points,where the integrand function f is known; iii) the number of the requested evaluations of f at the nodes is low,iv) a satisfactory convergence theory can be proved.

Research Progress on Detection Methods of Melamine and Cyanuric Acid Residue in Dairy Products and Feedstuff

[Objective] More accurate, rapid and sensitive method of melamine and cyanuricacid residue in dairy products and feedstuff were re- viewed. [ Method] Physicochemical properties, metabolism, uses, harm and detection methods of melamine and cyanuric acid were analyzed and described. [ Result] Melamine and cyanuric acid, when used alone, were slightly toxic, but long -term intake could lead to animal reproductive and urinary system damage. [ Condusion] Establishing a more sensitive, fast and easy to popularize detection method for elarnine and cyanuricacid res- idue in dairy products and feedstuff was necessary.