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Chemical-assisted steam flooding is a promising method for heavy oils. However, few researches have investigated the performance of nitrogen foam and gel-assisted steam flooding (NFG-A-SF) on offshore heavy oil reservoirs. In addition, the effects of operational parameters on the performance of NFG-A-SF are still unclear. In this study, a numerical simulation model was developed to simulate NFG-A-SF processes for offshore heavy oil reservoirs. Then, a comparison of NFG-A-SF and steam flooding was made to assess the feasibility of NFG-A-SF in a well group at Oilfield L, a typical offshore heavy reservoir of Bohai Bay, China. Finally, sensitivity analysis was conducted to investigate the effects of the chemical agent type, injection mode, injection timing, cycle number of gel and nitrogen foam, polymer concentration, crosslinking agent concentration, foaming agent concentration, and gas-liquid ratio on NFG-A-SF performance. The results showed that, compared with SF, the NFG-A-SF has better performance, which is a potential and effective method for offshore heavy oil reservoirs. The NFG-A-SF should be implemented as early as possible. During the NFG-A-SF processes, gel slugs should be injected alternately with nitrogen foam slugs, and the gel slugs should be injected first before nitrogen foam slugs. Considering the economic benefit, there are optimum cycle numbers of gel and nitrogen foam, gas-liquid ratio, concentrations of polymer, crosslinking agent, and foaming agent.

The application of TiO

Purpose Spray curing has become the preferred curing method for most cement concrete members because of its lower cost and sound effect. However, the spray curing quality of members is vulnerable to random variation environment factors and anthropogenic interferences. This paper aims to introduce the machine learning algorithm into the spray curing system to optimize its control method to improve the spray curing quality of members. Design/methodology/approach The critical parameters affecting the spray curing quality of members were collected through experiments, such as the temperature and humidity of the member's surface, the temperature, humidity and wind speed of the environment. The C4.5 algorithm was used as a weak classifier algorithm, and the AdaBoost.M1 algorithm was used to cascade multiple weak classifiers to form a robust classifier according to the collected data. Findings The results showed that the model constructed by the AdaBoost.M1 algorithm had achieved higher accuracy and robustness among the two algorithms. Based on the classification model built by the AdaBoost.M1 algorithm, the spray curing system can cause automatic decision-making spray switching according to the member's real-time curing state and environment. Originality/value With the classification model constructed by the AdaBoost.M1 algorithm, the spray curing system can overcome the disadvantages that external factors greatly influence the current control method of the spray curing system, and the intelligent control of the spray curing system was realized to a certain extent. This paper provides a reference for applying machine learning algorithms in the intellectual transformation of bridge construction equipment.

A oilfield, located in Iraq, is a large multi-layer oil field dominated by carbonate reservoirs of cretaceous Mishrif formation and Tertiary Asmari Formation. A oilfield is located in the transition zone between Zagros orogenic belt and the eastern edge of Arabian platform, mainly in the low-angle fold belt of Zagros structure. Due to the influence of the Zagros orogeny, the fault system of the oilfield is developed, especially the Asmari formation A. It is a complex carbonate reservoir composed of dolomite, limestone and sandstone. The plane and vertical heterogeneity are very strong. The development of micro-fractures is verified by core data, logging data and dynamic analysis. And the multiplicity of well test interpretation is obvious. The core, thin section, imaging logging and well test data (Fig.1) of A oilfield all show that fractures are developed in Asmari formation, and the production performance shows that the existence of fractures improves the production of oil wells. However, due to the serious breakthrough of edge water and secondary bottom water, the water cut of the oilfield rises rapidly, leading to the difficulty in ensuring the development effect of the oilfield. Moreover, the fracture scale in the study area is too small to characterize the parameters, the plane distribution of fractures is different and its distribution law is difficult to predict accurately, which has also become the key to restrict the efficient development of oil fields. As carbonate reservoir has developed fractures and strong heterogeneity, its pore structure is more complex than sandstone reservoir, and the uncertainty of parameter interpretation is higher. Therefore, how to evaluate and obtain the reservoir parameters of A oilfield has always been a great concern. In general, logging, core analysis and pressure transient analysis (PTA) are used to evaluate the reservoir parameters of carbonate rocks. However, core and logging analysis can be used to get static parameters in the range of centimeter to meter, while PTA can obtain static and dynamic parameters in the range of hundreds of meters to several kilometers, such as skin coefficient, boundary conditions, permeability and cross flow coefficient. Therefore, the PTA results are more practical.

The efficient development of oilfield mostly depends on a comprehensive optimization of subsurface flow. The development effect of water-flooding is affected by technology, economy and other aspects, so its development objective is not invariable. To account for several discrete or even contradicting objectives, dynamic multi-objective optimization evolutionary algorithm (DMOEA) presents multiple optimum solutions for decision-making processes. The primary goal of this work is to optimize well placement and control parameters based on multiple design objectives using reservoir production potential formula and surrogate-assisted dynamic multi-objective optimization evolutionary algorithm. A new workflow is introduced to optimize water-flooding strategy in presence of multiple conflicting criteria and time-depending constraints. The workflow consists of two optimization stages. First, we construct an improved reservoir production potential formula which considers factors such as oil saturation, pressure, fluid flow capacity, etc. The influence of dynamic seepage capacity and static reserve distribution of oil on reservoir production capacity is comprehensively evaluated by this formula. Optimal well placement can be guided based on production potential. Then, a robust computational framework that couples Deep Neural Network (DNN) and dynamic multi-objective optimizers to optimize the aforementioned objectives in water-flooding processes simultaneously. DNN is trained and employed as surrogate model of the high-fidelity simulator in the optimization workflow and DNSGA-II-A is employed to optimize control parameters by maximizing the overall oil production and NPV, and minimizing the water cut. The Pareto front arising from the above process provides many water-flooding scenarios yielding to practical decision-making capabilities. The performance of the proposed workflow is validated in Shengli Oilfield. The results demonstrate that the method can ensure the more reasonable optimization of the whole process of water-flooding. This work can provide not only the economic and technical solutions but the correct optimization responses according to the multiple design objectives. Besides, the robustness and convergence speed of this method is better than other algorithms. Compared with the traditional single-objective optimization algorithm, the proposed method can comprehensively consider the relationship between various development objectives, to give reasonable optimal solutions. Compared with the traditional static optimization algorithm, it can track the changing Pareto optimal front in time, to provide a diversified optimal solution set according to the needs of reservoir engineers. The major contribution of this work is the introduction of a new approach that can effectively balance the needs of various objectives such as benefit, cost, and risk in the life-cycle of water-flooding and make a rapid response. The presented reliable method could provide certain significance for the efficient optimization of well placement and control parameters in the oilfield.

This article aims to improve the toughness of pre-packaged grouts (PPG) by incorporating crumb rubber. The mechanism for toughness of PPG with crumb rubber was analyzed based on the uniaxial compression model. Crumb rubber with surfaces treated by different methods (NaOH solutions or microwave treatment) was observed by scanning electron microscopy (SEM). The effects of mesh sizes, amounts, surface-treated methods of crumb rubber, and mixing procedures on the PPG’s mechanical strength and rheological properties were investigated. The results showed that, firstly, the addition of crumb rubber improves the PPG’s toughness, while its mechanical strength is reduced. Adding NaOH solutions or microwave-treated crumb rubber into PPG can weaken the negative effects of crumb rubber on the PPG’s mechanical strength, however, this function is limited. Secondly, the crumb rubber grouts’ rheological properties can be fully exploited by increasing the stirring rate and time so that the fluidity of crumb rubber grouts is improved, which fulfils the characteristics of no bleeding and micro-expansion. Finally, the optimal formula and mixing technique of crumb rubber grouts were proposed in this paper.The results of this paper can provide a significant reference for the application of scrap tires.

This article presents an extensive relative permeability study for low salinity water flooding (LSWF) in minerally different carbonate rocks. The experiments were performed with unsteady state meth...

This article aims to improve the accuracy and predictability of rebound method in compressive strength evaluation of concrete members of the Yanchong Expressway, which is an important traffic guarantee project for the 2022 Winter Olympics Games in Zhangjiakou city, Hebei Province, China. For this purpose, concrete specimens were prepared by the mixing ratios of C35, C40, and C50. First of all, the paper investigated the effects of curing age, types, and dosage of mineral admixtures (fly ash and slag) on concrete rebound number and compressive strength. Then, the special testing strength curves of 7 d and 90 d curing age were proposed. The results showed that the average relative errors of the special testing strength curves were reduced by 35.99% and 20.26% respectively, compared with the national testing strength curve. Fly ash and slag were beneficial to the growth of concrete rebound numbers but cannot contribute to the growth of concrete compressive strength (except for fly ash single-mixed with 15% dosage). The present results can provide a significant reference for the compressive strength evaluation and later maintenance of concrete members of the Yanchong Expressway or other projects with tight construction period in this area.

In order to quantitatively study the applicability of needle/cone penetration experiment for asphalt-rubber (AR), the dynamic model of needle/cone in penetration process was established based on the Kelvin model, and the different impacts of crumb rubber (CR) particles on needle/cone penetration depth were analyzed using Matlab. The probability of needle/cone contacting CR particles in penetration process was statistically calculated. AR binders with different CR particle sizes were observed using scanning electron microscope (SEM) and prepared for testing needle/cone penetration. The results showed that the solid-liquid two-phase feature of AR binders gradually weakened with the reduction of CR particle size. At the same viscoelastic parameters, CR particles had little impact on cone penetration depth when cone contacted CR particles. The probability of cone contacting CR particles was close to 100% in penetration process while that of needle was around 50%. The standard deviations of the needle penetration experimental results for AR binders with #20, #30, #40, #60, and #80 mesh CR were 4.0, 3.8, 2.8, 1.1, and 1.1 times of cone penetration, respectively. This study shows that the cone penetration experiment has a significant advantage in evaluating the consistency of the AR binder with coarse CR.

Reservoir rock type (RRT) classification is commonly used for the fine characterization of strongly heterogeneous reservoirs. Many research focused on proposing different RRT methods, while few studies focused on the relationship between micropore structure and macroscopic seepage law of fluid transporting through different types of formation. Considering the different diagenesis degrees of rock types in different sedimentary facies, the quadrant unit method and FZI method were applied to classify and evaluate the strongly heterogeneous reservoirs. The corresponding capillary pressure curves and relative permeability curves for different rock types could be obtained. A set of theoretical models that could describe the macroscopic seepage law are proposed, and numerical simulation case studies were carried out to explore the superimposed seepage law in reservoirs under different degrees of heterogeneity. Results show that water cut rises faster in poor-property RRT formation than in good-property RRT formation, with a lower recovery factor under the same conditions. The water cut rising rate increases firstly and then decreases with water cut and recovery degree increasing. The poorer the rock property is, the earlier and higher the peak value of water cut rising rate is. Also, based on numerical simulation case studies, the superimposed seepage law is close to that in the poor-property formation as the heterogeneity degree strengthens. The newly proposed method not only could provide a reference for rock type classification based on micropore structure but also could expound the influence of micropore structure on the change law of macroscopic water cut and elaborates the macroscopic change law after the superposition of different types of reservoirs. The conventional normalization of relative permeability curves is only applicable to relatively homogeneous reservoirs and not applicable to strongly heterogeneous reservoirs, especially when the permeability variation is higher than 0.7. The newly proposed approach is capable of analyzing the effect of micropore structure on macroscopic seepage law and improving the prediction accuracy of the production profile.

Buckley–Leverett theory and Welge equation is one of the most widely used methods for predicting fluid transport in porous media. However, the average water saturation in Welge equation is a function of both water saturation and water cut, which is inconvenient to be used. While the linear relationship (the slope is 2/3) of the average water saturation and outlet water saturation proposed by Зфрос provides a simple method the oil–water viscosity ratio greatly limits the application. In this paper, based on the analytical relative permeability model and Welge equation, a new average water saturation equation with a variable (the outlet water saturation) was derived and applied in some cases under different relative permeability parameters and oil–water viscosity. Moreover, a simplified equation was proposed through certain data regression. Thus, the coefficient calculation method of the linear function was introduced based on the actual production data. Results showed a nonlinear relationship between the average water saturation and outlet water saturation, which is mainly related to water and oil relative permeability, and oil–water viscosity ratio. For the outlet water saturation higher than frontal saturation, the equation can be simplified to a linear function, which is the derivation of Зфрос equation. However, it is a function of both relative permeability and oil–water viscosity ratio instead of a constant slope of 2/3. In view of the fact that oil–water viscosity is mostly bigger than 10 in most reservoirs, the new model is an important supplement to Buckley–Leverett theory and Welge equation.

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

The main characteristic of the complicated carbonate reservoirs is notably strong heterogeneous, leading to a high uncertainty in formation parameter evaluation. The most reliable method for obtaining the dynamic parameters is well test interpretation. However, the well test curve shows similar characteristics for multi-layers reservoirs, dual-medium reservoirs, and carbonate reservoirs with lithology mixed sedimentation lithology. Sometimes the well test fitting result under the mentioned three kinds of models is satisfied, but the interpretation result is quite different. In order to reduce the parameter evaluation multiplicity, the synthetic identification and evaluation method for obtaining the physical parameters of the complicated carbonate reservoir was proposed, based on completion types, core analysis, lithology analysis, and well test results. The evaluation method distinguishes the different carbonate reservoir characteristics from similar well test responses by summarizing and classifying the completion method, reservoir fracture characteristics, and production logging test (PLT) results. The reliability of the proposed method is verified by an application of actual carbonate reservoir parameters evaluation. The proposed method can distinguish among multi-layers reservoirs, dual-medium, and complicated reservoirs with mixed sedimentation lithology whose main characteristic is that concavity existing in the pressure derivative curve. If the well test match results were satisfied enough which lead to the proposed method and process was ignored, the interpretation results and production performance prediction may deviate largely from the actual situation.

During the high water-cut period, water plugging treatment is a commonly selected development strategy to reduce the production decline rate. However, determination of the appropriate water-cut to implement water plugging is rarely studied and therefore it is often decided based on the experience of reservoir engineers, which could result in potential oil production loss and ineffective water plugging treatment. In this paper, an analytical model to predict water-cut increasing was derived and the increase in water-cut under different pressure drawdown multipliers was examined. Then, an approach to determine the critical water cut to apply plugging was proposed and the critical condition of water plugging in high water-cut formation was analyzed. Based on the sensitivity study conducted at different water-cut and different pressure drawdown multipliers, the lower oil viscosity leads to a lower critical plugging water cut, which implies that water plugging treatment might not be suitable for all types of reservoirs. Furthermore, this finding is practiced in actual oilfield extraction and the critical plugging water-cut calculation method does improve production performance. Thus, this analytical approach is capable to enhance oilfield production performance and optimize the effectiveness of water plugging.

Waterflooding is the most commonly used oilfields development method, and the injectivity is the most critical factor for determining injectors count. Injectivity is usually based on actual on-field test, but lack of theoretical research. An analytical model was proposed to evaluate the water injectivity and predict the variation law. The decisive factor of the injectivity variation law was revealed through further mathematical analysis, and three mathematical conditions were also analyzed, which determine the injectivity variation characteristic. The results show that water injectivity changes continuously, quite different from the prevailing view. The most important finding from the theoretical analysis was that the injectivity presents three behaviors: 1) Injectivity increasing, 2) Injectivity decreasing and 3) Injectivity that decreasing at first and then increasing (an inflection point exsiting). The reasonability and precision of the analytical model were furtherly verified by the numerical cases study, injectivity test and actual injection practice, and could be used for injectivity forecasting. For different viscosity oil reservoirs, the variation law is different and the engineering design should be different, the development effect and economic benefit would be furtherly improved.

The possibility that the productivity is seriously reduced if the producing formation is exposed to the fresh water has been recognized for many years. Many experiments and theoretical studies have been carried out to elucidate the mechanism of water sensitivity. Despite years of field observations and laboratory research, methods of predicting quantitatively the susceptibility of formation to water-sensitivity have not been developed to complete reliability. Until now, there was no model to predict the productivity decreasing according to the experiment results except time consuming simulation. Based on the latest researches on water sensitivity characteristics and mechanism, core experiments were set up to analyze the permeability and relative permeability parameters changing along with the salinity decreasing under different reservoir properties. Based on material balance equation, analytical function of relative permeability, Backley Leverett theory and Welge equation, a new productivity decline model which can quantitatively predict water sensitivity influence is derived with all the coefficients clearly. The new model has been proven to accurately predict the production performance. The changing coefficients could be obtained through analyzing permeability loss and relative permeability changes through laboratory measurements. The most significant innovation is that the production performance could be quantitatively predicted rely on the laboratory results. It can be concluded from experiment analysis that the permeability loss was slow after the first fast, and the relative permeability parameters also changed (residual oil saturation and irreducible water saturation becomes bigger, and the relative permeability of both oil and water phase becomes smaller at the same water saturation) along with salinity decreasing because of the clay swelling and the colloidal clay particles captured at a local pore constriction. Then the change of the coefficients in new model could be calculated and the production performance could be predicted. The proposed model has been tested using production data from several reservoirs and the results have demonstrated that the proposed new model works satisfactorily. The novelty of the new dynamics model is in the ability to solve the production prediction approach for water-sensitive reservoirs according to experiment measurements without time consuming simulation.

ECPT is a novel technology which utilizes capacitive coupling to realize wireless energy transfer. But, it is well known that ECPT system is very sensitive to parameters variation. In order to improve system robustness, a novel quasi sliding mode control method is proposed. A GSSA model is building up and a quasi sliding mode variable structure control strategy is proposed accordingly. Finally, the sliding mode control method is verified by experimental results.

In order to improve the quality control level of rubber asphalt production process, and reduce the performance variability of rubber asphalt, the dynamic quality monitoring system of rubber asphalt production process was developed in this paper, through which the real-time monitoring, real-time recording and remote on-line monitoring of the production process of rubber asphalt were realized. Application shows that the technology can reduce the performance variability of rubber asphalt and improve the construction quality and efficiency of rubber asphalt pavement. It has positive roles in improving quality control level of asphalt rubber production process, and promoting the application of rubber asphalt in highway industry.