Quantitative Prediction Method Research Articles

Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network.

Business development is dependent on a well-structured human resources (HR) system that maximizes the efficiency of an organization's human resources input and output. It is tough to provide adequate instructions for HR's unique task. In a time when the domestic labor market is still maturing, it is difficult for companies to make successful adjustments in HR structures to meet fluctuations in demand for human resources caused by shifting corporate strategies, operations, and size. Data on corporate human resources are often insufficient or inaccurate, which creates substantial nonlinearity and uncertainty when attempting to predict staffing needs, since human resource demand is influenced by numerous variables. The aim of this research is to predict the human resource demand using novel methods. Recurrent neural networks (RNNs) and grey wolf optimization (GWO) are used in this study to develop a new quantitative forecasting method for HR demand prediction. Initially, we collect the dataset and preprocess using normalization. The features are extracted using principal component analysis (PCA) and the proposed RNN with GWO effectively predicts the needs of HR. Moreover, organizations may be able to estimate personnel demand based on current circumstances, making forecasting more relevant and adaptive and enabling enterprises to accomplish their objectives via efficient human resource planning.

Research on a hydrate saturation prediction method based on an analysis of factors that influence the prediction accuracy of sea area hydrate saturation

It has been predicted that China has about 80 billion tons of oil equivalent of natural gas hydrate resources in its sea. China has conducted at least six drilling and two trial exploitations in the South China Sea, achieving good results. However, these achievements do not necessarily mean that the research area can support commercial exploitation. Only when the natural gas hydrate resources in the research area reach a certain scale will it be valuable for commercial exploitation. A reliable quantitative prediction method is necessary to clarify the scale of gas hydrate in the research area. However, the classical Wood method’s application to the prediction of suspended hydrate saturation in the Shenhu maritime area of China results in a large prediction error; the analysis shows that an unreliable measurement of reservoir parameters is the main reason for the large prediction error. In order to clarify the influence of reservoir parameters, this paper—by analyzing the measurement sources of the parameters of reservoir parameters, firstly indicates that the inaccurate measurement of three reservoir parameters—matrix composition, porosity, and density—is the main cause of prediction error. Then, using the design of different reservoir parameter measurement schemes to conduct comparative analysis, this paper points out that unreliable porosity and density measurement may lead to large prediction errors, while unreliable measurements of matrix composition have a relatively small impact on prediction accuracy. Further analysis shows that the absolute value of the prediction error caused by uncertainty in synthetic reservoir parameter measurement is sometimes larger than the sum of the absolute prediction error caused by the single parameter measurement uncertainty. In addressing the problem of large prediction errors caused by the inaccurate measurement of reservoir parameters, this paper proposes a hydrate saturation prediction method based on non-hydrate correction—called the “Wood-Bao method”. Simulation and actual data studies show that the prediction effect of this method is superior to that of the Wood method.

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Compared with shale gas in north American, it is older, more mature, and significantly difference in China. Taking the Longmaxi Formation of WY block in Sichuan Basin as an example, the quantitative prediction method of sweet spots for high mature shale gas is formed. Firstly, the geophysical response characteristics of shale gas reservoir is studied, and the physical characteristics of the sweet spots are determined. Then, the distributions of reservoir evaluation parameters, such as total organic carbon, brittleness, and others, are obtained by pre-stack simultaneous inversion. Finally, fuzzy neural network method is carried out to predict the distribution of sweet spots and to deploy wells. Shale gas sweet spots in this block vertically concentrate within 30 m above the bottom of the Longmaxi Formation. And two grades of sweet spots are evaluated. The quantitative relationship between the production results of horizontal wells and the evaluation is established with Fuzzy neural network method.

Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification.

Performance prediction based on candidates and screening based on predicted performance value are the core of product development. For example, the performance prediction and screening of equipment components and parts are an important guarantee for the reliability of equipment products. The prediction and screening of drug bioactivity value and performance are the keys to pharmaceutical product development. The main reasons for the failure of pharmaceutical discovery are the low bioactivity of the candidate compounds and the deficiencies in their efficacy and safety, which are related to the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the compounds. Therefore, it is very necessary to quickly and effectively perform systematic bioactivity value prediction and ADMET property evaluation for candidate compounds in the early stage of drug discovery. In this paper, a data-driven pharmaceutical products screening prediction model is proposed to screen drug candidates with higher bioactivity value and better ADMET properties. First, a quantitative prediction method for bioactivity value is proposed using the fusion regression of LGBM and neural network based on backpropagation (BP-NN). Then, the ADMET properties prediction method is proposed using XGBoost. According to the predicted bioactivity value and ADMET properties, the BVAP method is defined to screen the drug candidates. And the screening model is validated on the dataset of antagonized Erα active compounds, in which the mean square error (MSE) of fusion regression is 1.1496, the XGBoost prediction accuracy of ADMET properties are 94.0% for Caco-2, 95.7% for CYP3A4, 89.4% for HERG, 88.6% for hob, and 96.2% for Mn. Compared with the commonly used methods for ADMET properties such as SVM, RF, KNN, LDA, and NB, the XGBoost in this paper has the highest prediction accuracy and AUC value, which has better guiding significance and can help screen pharmaceutical product candidates with good bioactivity, pharmacokinetic properties, and safety.

Analysis of hydro-abrasive erosion in Pelton buckets using a Eulerian-Lagrangian approach

One major concern in developing high-head water resources, which usually uses Pelton turbines, especially in geologically young and fragile mountains, is hydro-abrasive erosion. However, the physical mechanism of hydro-abrasive erosion in Pelton turbine buckets is still not well understood as the cumulative erosion is changing with the transient flow in rotating buckets. In this work, a new approach to predict the cumulative erosion on a rotating surface is proposed and applied to Pelton turbine buckets. This method is based on the Eulerian-Lagaraigian approach previously developed by authors and a novel algorithm introduced to predict the impact behavior of particles hitting rotating surfaces. The numerically obtained erosion is validated with 3D-scanned erosion profiles of buckets measured in a prototype plant. Further, a detailed analysis of the flow and hydro-abrasive erosion characteristics with bucket rotation is conducted. The good agreement with field values demonstrates the promising ability of the proposed method for quantitative erosion prediction. Interestingly, the flow interferences between the jet and the water sheet are found to produce a shield layer to prevent particles from directly impacting surfaces and consequently reduce hydro-abrasive erosion. The current work may provide important engineering insights for geometry optimization to reduce hydro-abrasive erosion.

Investigation on high-frequency and mode-coupling vibrations in thickness-extensional piezoelectric thin-film resonators with initial stress

Mode-coupling vibrations in thickness-extensional piezoelectric thin-film bulk acoustic wave resonators (FBARs) with initial stress operating in the ultra-high frequency (UHF) range are investigated by employing the Frequency Spectrum Quantitative Prediction (FSQP) method. The uniaxial uniform initial stresses along the in-plane and thickness directions are separately considered. The dispersion relation of the bulk wave in the FBAR with initial stress is obtained and then the solutions of physical fields are constructed by superimposing the eigen-modes in the dispersion curve. The variational formulation considering the initial stress is derived through the perturbation method. Then frequency spectra that predict the coupling strength between the thickness-extensional mode and other eigen-modes are obtained by substituting the solutions into the variational formulation. Mode shapes of mechanical displacements are presented to illustrate the influence of initial stress on mode-coupling behaviors in FBARs. The results show that the initial stress could cause the shifts of frequency spectrum curves, reflecting that the in-plane initial stress leads to the shifts of spectrum curves along the lateral aspect ratio axis, while the out-of-plane initial stress leads to the shifts along the frequency axis (i.e., frequency shifts) and lateral aspect ratio axis simultaneously. Desirable structural parameters should be chosen based on frequency spectra to avoid the increases of mode-coupling strength induced by initial stress. The out-of-plane initial stress could be used to tune the resonance frequency of the FBARs without changing the mode-coupling behaviors. The coupling strength factor is first defined for the convenient and fast judgment of mode-coupling intensity of all data points in the frequency spectra. The obtained results provide a crucial reference to the manipulation of resonance frequency and the mode-coupling strength in FBAR devices.

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions

Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol−1) relative to those calculated at ωB97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients {k}_{infty }(T) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.

Novel Method for Investigating the Correlation of Sensory Contribution and Cigarette Mainstream Smoke

The quality of tobacco products is directly related to the sensory contributions of related compounds in the mainstream smoke during tobacco combustion. Currently, there is no clear and perfect quantitative calculation method for tobacco quality assessment and prediction. In this paper, samples of Qujing K326 and Dali Hongda were used as raw materials to simulate an actual tobacco smoke release environment. The mainstream smoke products were captured, separated and analyzed by gas chromatography-mass spectrometry (GC-MS). The relative percentage of each compound in mainstream tobacco smoke was obtained. Therefore, through relevant quantitative calculation, this study quantified the sensory contributions of compounds through flue gas separation. Finally, the evaluation quality of tobacco samples could be predicted accurately by neural network model. The methods used in this paper can provide important technical support for tobacco quality control.

Predictions of Dynamic Multimode-Coupling and High-Frequency Vibrations in Magneto-Electro-Elastic Heterostructures.

Mode coupling between the operation mode and unwanted eigenmodes has a significant influence on the working performance of novel thin-film magnetoelectric (ME) devices operating at high frequencies. In this article, the extended frequency spectrum quantitative prediction (FSQP) method is used to investigate mode-coupling vibrations in high-frequency ME heterostructures. This method has three key procedures. First, wave propagation in ME heterostructures is studied to determine the wavenumber and frequency of the eigenmodes. Second, the variational formulation of a general ME heterostructure is constructed. Finally, frequency spectra for predicting the coupling strength among the eigenmodes are obtained by substituting the solutions consisting of all eigenmodes into the variational formulation. Two numerical examples are presented to validate the extended FSQP method. The mode shapes of the mechanical displacements are used to thoroughly describe the mode-coupling behavior in different vibration modes. The numerical results show that the mode-coupling strength is significantly affected by the structural size and number of layers in an ME heterostructure. Furthermore, structural symmetry along the thickness direction may cause specific mode-decoupling phenomena. Effective strategies for suppressing multimode-coupling vibrations in ME heterostructures by optimizing the lateral aspect ratios based on the frequency spectra are proposed to guide device design.

Prediction Method for the Ground Vibration Damage Boundary of Thick and Hard Rock Layer Fracture Type Mine Quakes

In this study, based on the example of the ground vibration damage response of a thick and hard rock layer fracture type mine quake, and by applying the theories of mine pressure, rock mechanics and vibration energy principle, the concept of “vibration damage boundary” of mine quake ground, which uses particle vibration velocity to evaluate the vibration damage, is proposed. In addition, the quantitative prediction method of the ground vibration damage boundary is preliminarily established. The research results reveal that the elastic deformation of the fixed support end of the thick and hard rock layer structure in the stope is the main energy source for the formation of mine quakes, and that the particle vibration velocity caused by the propagation of focal energy to the ground can reasonably reflect the response degree of vibration damage. The proposed prediction method considers the “instantaneous” motion characteristics of mining thick and hard rock layer and the dynamic load effect of “mine quakes.” The method can deepen our understanding of mining ground damage prediction, and increase the reliability of ground damage boundary prediction. Finally, the results are used to predict the ground vibration damage boundary of limestone primary fracture in the longwall 16101 face of the Fuping Coal Mine.

A risk prediction method of pipe sticking accidents due to wellbore uncleanness for long horizontal section wells

The prediction of pipe sticking accidents due to wellbore uncleanness is significant for safe drilling, while existing qualitative prediction methods result in inaccuracy and lateness. To cover the shortages, a novel quantitative pipe sticking prediction method is established to achieve pipe sticking prediction early in drilling preparatory stage. First, the additional axial forces and torques on the drill string exerted by accumulated cuttings are calculated in tripping out, tripping in, back reaming and reaming processes based on granular mechanics. Second, the torque and drag model is modified considering additional axial forces and torques to calculate the hook load and the top drive torque. Finally, the pipe sticking prediction is conducted according to the critical hook load and the critical top drive torque in a specific drilling process. A case study including comparison with the classical model and sensitivity analysis is conducted. Under the given condition, pipe sticking risk is predicted with the new mothed by anticipating hook loads (5524.67 and −667.71 kN) and top drive torques (60.87 and 66.36 kN m) exceeding the critical values. The hook load and the top drive torque change exponentially with the travelling distance. In tripping out and back reaming processes from 4350 to 4250 m, the hook load and the top drive torque respectively increase for 1894.36 kN and 53.99 kN m. In tripping in and reaming processes from 3450 to 3550 m, the hook load decreases for 29925.66 kN and the top drive torque increases for 157.3 kN m. According to the sensitivity analysis, low frictional coefficients, initial cutting volume fractions, torque and drag frictional factors and high junk slot ratios are required to reduce the risk of pipe sticking. This study provides a timely and quantitative prediction method for pipe sticking accidents due to wellbore uncleanness in long horizontal drilling.

Queueing-Theoretic Performance Analysis of a Low-Entropy Labeled Network Stack

Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the Labeled Network Stack (LNS) based server is a good case to gain orders of magnitude performance improvement compared to servers based on traditional network stacks. However, it is desirable to figure out 1) where the low tail latency and the low entropy of LNS mainly come from, compared with mTCP, a typical user-space network stack in academia, and Linux network stack, the mainstream network stack in industry, and 2) how much LNS can be further optimized. Therefore, we propose a queueing theory-based analytical method defining a bottleneck stage to simplify the quantitative analysis of tail latency. Facilitated by the analytical method, we establish models characterizing the change of processing speed in different stages for an LNS-based server, an mTCP-based server, and a Linux-based server, with bursty traffic as an example. Under such traffic, each network service stage's processing speed is obtained by non-intrusive basic tests to identify the slowest stage as the bottleneck according to traffic and system characteristics. Our models reveal that the full-datapath prioritized processing and the full-path zero-copy are primary sources of the low tail latency and the low entropy of the LNS-based server, with 0.8%-24.4% error for the 9 9 t h percentile latency. In addition, the model of the LNS-based server can give the best number of worker threads querying a database, improving 2.1 × -3.5 × in concurrency.

Quantitative prediction of additive manufacturing deposited layer offset based on passive visual imaging and deep residual network

In the additive manufacturing process, the actual position of the welding torch will affect the shape of the weld pool, thereby affect the welding quality. So online monitoring of the deposited layer offset is essential. Based on the visual morphology of the molten pool obtained by the passive vision sensor, this paper proposes a quantitative prediction method for the deposition layer offset, during the arc additive manufacturing process. In order to prove the accuracy and effectiveness of this prediction method, this paper uses the deposited layer data to construct a model, and then predicts the offset. On this basis, the deviation direction of the initial deposited layer was changed, thus the generalization ability of the prediction method is verified. The experimental results show that the average prediction error of the deposited layer offset is less than 0.14 mm, while the network takes about 5 ms to process molten pool image. It proves that the proposed prediction method can be used to monitor the deposited layer offset real-time.

A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM2.5 Removal Modes on a Micro-Temporal Scale

PM2.5 is unanimously considered to be an important indicator of air quality. Sustained rainfall is a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. During sustained rainfall, the variation of PM2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale, missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting. This paper proposed a quantitative modeling and prediction method for sustained rainfall-PM2.5 removal modes on a micro-temporal scale. Firstly, a set of quantitative modes for sustained rainfall-PM2.5 removal mode in a micro-temporal scale were constructed. Then, a mode-constrained prediction of the sustained rainfall-PM2.5 removal effect using the factorization machines (FM) was proposed to predict the future sustained rainfall removal effect. Moreover, the historical observation data of Nanjing city at an hourly scale from 2016 to January 2020 were used for mode modeling. Meanwhile, the whole 2020 year observation data were used for the sustained rainfall-PM2.5 removal phenomenon prediction. The experiment shows the reasonableness and effectiveness of the proposed method.

Bayesian Inference for Predicting Rock Mass Rating Ahead of Tunnel-Face Excavation Integrating Multi-Source Information

The rock mass rating (RMR14) system can effectively evaluate the quality of the rock mass surrounding a tunnel, which combines the characteristics of the rock mass strength and the discontinuity. However, it is difficult to infer the RMR14 value accurately due to the data often being limited, as well as there being extensive data sources of geological exploration. The rock mass classification systems, which are known as empirical methods, are still used in current engineering design and construction. In this paper, a quantitative prediction method of the RMR14 value on tunnel excavation is proposed that is based on the Bayesian network theory. A variety of empirical formulas between the RMR14 with the geological strength index and the basic quality method are fused in the Bayesian network fused framework; MATLAB is used to generate 500 random samples, and the conditional probability table of the Bayesian network is generated based on the expected maximum algorithm. The uncertainty of the RMR value prediction is quantified by the RMR14 Progressive Prediction Probability Model. The proposed method is applied to a rock tunnel project, i.e., the Laoying tunnel in Yunnan province, China. The results show that this method is able to provide a reasonable probability inference of the RMR14.

Logic Analysis of Economic Forecast

Since ancient times, prediction has existed in human activities in different forms, which is the instinctive reaction of human survival, and logic is the rational thinking activity of judgment, reasoning and demonstration in human brain. With the development of society, prediction has experienced a scientific process from ignorance relying on intuitive experience to logical reasoning, and formed prediction logic. In the field of economic activities, prediction logic has its unique logical characteristics. On this basis, qualitative and quantitative prediction methods are produced. The combination of the two prediction methods reflects the alternation of different logical reasoning processes, and also makes the economic prediction conclusion more reliable, so as to avoid risks and improve the decision-making level.

Diagenetic evolution sequence and pore evolution model of Mesoproterozoic Xiamaling organic-rich shale in Zhangjiakou, Hebei, based on pyrolysis simulation experiments

Organic-rich shale is an unconventional and complex petroliferous system that integrates a “source-reservoir-cap”, and the coupled evolution of hydrocarbon generation, diagenesis and nanoscale pores is the key problem that affects shale gas accumulation. Current research methods for this issue mainly include direct observation and physical simulation. Direct observation ignores the heterogeneity and regional differences of the natural samples, and physical simulation mostly lacks an intuitive characterization and cannot clearly show the relationship between minerals and pore evolution characteristics in the same region. To avoid the effect of heterogeneity on the researched samples and clearly and intuitively reveal the coupled evolutionary relationship among the hydrocarbon generation of thermal maturation, diagenesis and nanopore structures in shale, this research included hydrous pyrolysis experiments on low mature marine shale samples to achieve various maturities and diagenesis stages. The pyrolysis products at each stage were recovered and subjected to an ongoing multidisciplinary analytical program. The results show that an increased temperature intensifies the evolution of dissolution pores in unstable brittle minerals, promotes clay mineral conversion, and accelerates the development of clay mineral pores and organic pores. The pore volume (PV) of the nanoscale pores reached its minimum at 300 °C, increased to its maximum at 500 °C and decreased thereafter. The surface area (SA) of the nanoscale pores reached their minimum at 300 °C and then continuously increased, while those of macropores reached their maximum at 500 °C and then decreased. The diagenesis of the pyrolysis products were mainly dissolution, clay mineral transformation, the thermal maturation of organic matter (OM), compaction and cement filling. The diagenetic evolution was divided into four stages, and the diagenetic evolution sequence and pore evolution model of the shale were established. Hydrocarbon generation, diagenesis and nanoscale pore evolution have synergistic effects, which have great significance for shale reservoir evaluation and shale gas accumulation, exploration and exploitation. The conceptual evolution model provides a quantitative prediction method for hydrocarbon generation, diagenesis and nanoscale pore variations.

Predicting the relationship between PFAS component signatures in water and non-water phases through mathematical transformation: Application to machine learning classification

Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, as a result of decades of use across a range of applications. While PFAS contamination often enters the environment in the aqueous phase, PFAS is regularly detected in a range of different phases, including soils, sediments and biota. Although PFAS at a given site may originate from the same sources, the compositions observed in different phases are nearly always different, a fact that can complicate source allocation efforts. This paper presents a quantitative method for prediction of the relative composition of PFAS in different phases for components for which differences in behavior are primarily driven by hydrophobicity. The derived equations suggest that under these conditions, the relative compositions in different phases in contact with water should be independent of overall affinity for the phase, and as such should be the same for all non-water phases. This result is illustrated with data from individual samples, as well as from site-wide evaluations for a range of different phases. The results of the work provide a useful tool to reconcile PFAS composition differences in different phases, and provide a baseline for recognizing cases where hydrophobicity is not the primary driver of differences in distribution between phases. Furthermore, the results may be useful in forensic applications for classification of PFAS across different phases. The use of the resulting equations to transform water data to train a supervised learning algorithm for forensic analysis of PFAS in non-water phases is illustrated.

Quantitative hydrate deposition prediction method and application in deep-water or permafrost gas pipelines

Hydrate formation in pipelines seriously affects the efficiency of energy transmission. Adopting the production process of gas pipelines in deep-water or permafrost regions as the engineering application background, hydrate formation and deposition characteristics at different locations in gas pipelines are analyzed to realize the quantitative prediction of high-risk areas and degree of hydrate plugging in pipelines. A typical deep-water gas pipeline in the South China Sea is selected as an example. The calculation results show that the long-distance pipeline exhibits an obvious hydrate blockage risk area, which occurs 700-3200 m away from the pipeline inlet. The high-risk area of hydrate plugging in the system moves upstream of the pipeline with decreasing ambient temperature. The risk of hydrate blockage increases with decreasing pipeline inner diameter. The established quantitative prediction method for hydrate deposition in deep-water or permafrost gas pipelines accurately locates high-risk areas of hydrate plugging, which could effectively guide hydrate prevention and notably reduce the cost of hydrate control in deep-water or permafrost gas pipelines.

Determination of reservoir wettability based on resistivity index prediction from core and log data

With the increasing demand of precisive reservoir characterization in exploration and development of oil and gas, wettability plays a more and more significant role because it influences oil and water distribution. The determination of wettability has been a challenge to the researchers focusing on this area. In this study, a new method is proposed for the determination of wettability based on resistivity index prediction. Firstly, the model for estimating resistivity index using capillary pressure, porosity and median pressure by J-function is derived. Secondly, the model to estimate resistivity index via T2 time, porosity and T2 geometric mean is derived, which combines J-function with Schlumberger-Doll-Research (SDR) model. Thirdly, a new parameter, the ratio of mean saturation index calculated from first method to the one calculated from second method, is used to determine the reservoir wettability. To calibrate and verify the established models, 30 cores were acquired for the petrophysical experiments. The experimental results of 25 cores are used to calibrate the established models and build the quantitative relationship between the wettability and the ratio of mean saturation index, with a relevance of 0.96. Besides, the calibrated model is tested by using the experiment data of the rest 5 cores. The estimated wettability conforms to the experimental results. Finally, the processing and interpreting results of log data also indicate that the proposed model for quantitative prediction of wettability features good application effect. In conclusion, the proposed method for quantitative prediction of wettability is effective and highly reliable, which solves a difficult problem in formation evaluation.