Integrated Reservoir Prediction and Oil-Gas Evaluation in the Maoshan Area

The Maoshan area is an area with well-developed igneous rocks and complex structures. The thickness of the reservoirs is generally small. The study of the reservoirs is based on seismic data, logging data and geological data. Using techniques and software such as Voxelgeo, BCI, RM, DFM and AP, the authors have made a comprehensive analysis of the lateral variation of reservoir parameters in the Upper Shazu bed of the third member of the Palaeogene Funing Formation, and compiled the thickness map of the Shazu bed. Also, with the data from ANN, BCI and the abstracting method for seismic characteristic parameters in combination with the structural factors, the authors have tried the multi-parameter and multi-method prediction of petroleum, delineated the potential oil and gas areas and proposed two well sites. The prediction of oil and gas for Well JB2 turns out to be quite successful.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Application of Machine Learning for Flood Prediction and Evaluation in Southern Nigeria

This study explored the application of machine learning techniques for flood prediction and analysis in southern Nigeria. Machine learning is an artificial intelligence technique that uses computer-based instructions to analyze and transform data into useful information to enable systems to make predictions. Traditional methods of flood prediction and analysis often fall short of providing accurate and timely information for effective disaster management. More so, numerical forecasting of flood disasters in the 19th century is not very accurate due to its inability to simplify complex atmospheric dynamics into simple equations. Here, we used Machine learning (ML) techniques including Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN) to model the complex physical processes that cause floods. The dataset contains 59 cases with the goal feature “Event-Type”, including 39 cases of floods and 20 cases of flood/rainstorms. Based on comparison of assessment metrics from models created using historical records, the result shows that NB performed better than all other techniques, followed by RF. The developed model can be used to predict the frequency of flood incidents. The majority of flood scenarios demonstrate that the event poses a significant risk to people’s lives. Therefore, each of the emergency response elements requires adequate knowledge of the flood incidences, continuous early warning service and accurate prediction model. This study can expand knowledge and research on flood predictive modeling in vulnerable areas to inform effective and sustainable contingency planning, policy, and management actions on flood disaster incidents, especially in other technologically underdeveloped settings.

Preliminary Evaluations of FGOALS-g2 for Decadal Predictions

The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature （SAT）, particularly in that of Nifio3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate （the control） by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 （LICOM2）. The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.

An scientific evaluation of annual earthquake prediction ability

The scientific idea of earthquake prediction in China is introduced in this paper. The various problems on evaluation of earthquake prediction ability are analyzed. The practical effect of prediction on annual seismic risk areas in 1990~2000 in China is discussed based on R-value evaluation method, and the ability of present earthquake prediction in China is reviewed.

Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy

The evaluation of the implementation effect of the power substation project can find out the problems of the project more comprehensively,which has important practical significance for the further development of the power substation project.To ensure accuracy and real-time evaluation,this paper proposes a novel hybrid intelligent evaluation and prediction model based on improved TOPSIS and Long Short-Term Memory(LSTM)optimized by a Sperm Whale Algorithm(SWA).Firstly,under the background of considering the development of new energy,the influencing factors of power substation project implementation effect are analyzed from three aspects of technology,economy and society.Moreover,an evaluation model based on improved TOPSIS is constructed.Then,an intelligent prediction model based on SWA optimized LSTM is designed.Finally,the scientificity and accuracy of the proposed model are verified by empirical analysis,and the important factors affecting the implementation effect of power substation projects are pointed out.

Evaluation and prediction of earth pressure balance shield performance in complex rock strata:A case study in Dalian,China

This research explores the potential for the evaluation and prediction of earth pressure balance shield performance based on a gray system model.The research focuses on a shield tunnel excavated for Metro Line 2 in Dalian,China.Due to the large error between the initial geological exploration data and real strata,the project construction is extremely difficult.In view of the current situation regarding the project,a quantitative method for evaluating the tunneling efficiency was proposed using cutterhead rotation(R),advance speed(S),total thrust(F)and torque(T).A total of 80 datasets with three input parameters and one output variable(F or T)were collected from this project,and a prediction framework based gray system model was established.Based on the prediction model,five prediction schemes were set up.Through error analysis,the optimal prediction scheme was obtained from the five schemes.The parametric investigation performed indicates that the relationships between F and the three input variables in the gray system model harmonize with the theoretical explanation.The case shows that the shield tunneling performance and efficiency are improved by the tunneling parameter prediction model based on the gray system model.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.

Comprehensive Evaluation and Prediction of Enhancement of Boiling Heat Transfer with Additives

A model of evaluation and prediction of enhancement of boiling heat transfer with additives has been proposed according to fuzzy fundamentals. Correlative appraisement of boiling heat transfer augmentation was done with the model based on 39 additives which were tested by the authors and other researchers. The results show that the evaluation of 35 additives is consistent with experiments, which means that the accuracy of the model is 89.7 percent. In addition, the prediction of the ability of boiling heat transfer enhancement with sodium oleate, polyethylene glycol and Tween-40 is also in good agreement with correspondent experiments.

A Machine Learning Based Funding Project Evaluation Decision Prediction

Traditional linear statistical methods cannot provide effective prediction results due to the complexity of human mind.In this paper,we apply machine learning to the field of funding allocation decision making,and try to explore whether personal characteristics of evaluators help predict the outcome of the evaluation decision?and how to improve the accuracy rate of machine learning methods on the imbalanced dataset of grant funding?Since funding data is characterized by imbalanced data distribution,we propose a slacked weighted entropy decision tree(SWE-DT).We assign weight to each class with the help of slacked factor.The experimental results show that the SWE decision tree performs well with sensitivity of 0.87,specificity of 0.85 and average accuracy of 0.75.It also provides a satisfied classification accuracy with Area Under Curve(AUC)=0.87.This implies that the proposed method accurately classified minority class instances and suitable to imbalanced datasets.By adding evaluator factors into the model,sensitivity is improved by over 9%,specificity improved by nearly 8%and the average accuracy also increased by 7%.It proves the feasibility of using evaluators’characteristics as predictors.And by innovatively using machine learning method to predict evaluation decisions based on the personal characteristics of evaluators,it enriches the literature in the field of decision making and machine learning field.

Grading evaluation and prediction of fracture-cavity reservoirs in Cambrian Longwangmiao Formation of Moxi area,Sichuan Basin,SW China

By using core, thin section, well logging, seismic, well testing and other data, the reservoir grading evaluation parameters were selected, the classification criterion considering multiple factors for carbonate reservoirs in this area were established, and the main factors affecting the development of high quality reservoir were determined. By employing Formation MicroScanner Image(FMI) logging fracture-cavity recognition technology and reservoir seismic waveform classification technology, the spatial distribution of reservoirs of all grades were predicted. On the basis of identifying four types of reservoir space developed in the study area by mercury injection experiment, a classification criterion was established using four reservoir grading evaluation parameters, median throat radius, effective porosity and effective permeability of fracture-cavity development zone, relationship between fracture and dissolution pore development and assemblage, and the reservoirs in the study area were classified into grade I high quality reservoir of fracture and cavity type, grade II average reservoir of fracture and porosity type, grade Ⅲ poor reservoir of intergranular pore type. Based on the three main factors controlling the development of high quality reservoir, structural location, sedimentary facies and epigenesis, the distribution of the 3 grades reservoirs in each well area and formation were predicted using geophysical response and percolation characteristics. Follow-up drilling has confirmed that the classification evaluation standard and prediction methods established are effective.

Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

Study on role of serum electrolyte combined with GFR evaluation equation in predicting the prognosis of severe obstructive renal injury

Objective: To study the predictive value of serum electrolyte combined with glomerular filtration rate (GFR) evaluation equation for prognosis of severe obstructive renal injury. Methods: A total of 69 patients with calculous obstructive renal impairment admitted to our hospital from May 2017 to December 2018 were selected as the research objects. Clinical data of the patients were collected, and according to the status of renal function impairment, they were divided into 37 cases of mild to moderate, 32 cases of severe, and 40 cases of health examination in the same period as the control group. The fasting serum of the subjects was separated in the morning, and the serum electrolytes and related indicators were detected by Olympus AV640 automatic biochemical analyzer, Scr-CysC GFR evaluation equation was used to calculate the GFR score of all subjects, the levels of serum sodium, potassium and GFR scores in patients with severe obstructive renal injury with different prognostic outcomes were analyzed, subject operating characteristic curve (ROC) of prognostic indicators in patients with severe obstructive renal impairment was drawn, and the prognostic values of serum Na+, K+, GFR score and their combination in patients with severe obstructive renal damage were analyzed. Results: Compared with the control group, the levels of UmAb, CysC, Scr, BUN, TC, serum sodium and potassium in mild to moderate group and severe group increased in turn, and the GFR score decreased in turn (P < 0.05). The serum sodium and potassium concentrations increased in turn and the GFR score decreased in turn at 1 month after operation (P < 0.05). Compared with the 1 day before operation, the serum sodium and potassium concentrations in mild group and severe group decreased and the GFR score increased 1 month after operation (P < 0.05). Compared with the good prognosis group, the serum sodium and potassium levels in patients with severe obstructive renal damage in the poor prognosis group increased significantly, and the GFR score decreased significantly (P<0.05). The results of ROC showed that the combined detection of Na + concentration, K+ concentration and GFR score had an AUC of 0.936 for predicting the prognosis and outcomes of patients with severe renal injury, which was significantly higher than that of the single detection (AUC of 0.796, 0.815 and 0.810, respectively). Conclusion: The serum sodium and potassium levels in patients with severe obstructive renal impairment are increased, and the GFR score is decreased. The combined detection of the three factors has certain reference value in predicting the poor prognosis of patients with severe obstructive renal impairment.

Application of Prediction Techniques in Carbonate Karst Reservoir in Tarim Basin

Carbonate karst reservoir is the emphases of Tarim＇s carbonate exploration. However, it is buried at a large depth, which results in Weak seismic reflection signal and low S/N ratio. In addition, the karst reservoir contains great heterogeneity, so reservoir prediction is very difficult. Through many years of research and exploration, we have established a suite of comprehensive evaluation technology for carbonate karst reservoir using geophysical characteristics and a geological concept model, including a technique for reconstructing the paleogeomorphology of buried hills based on a sequence framework, seismic description of the karst reservoir, and strain variant analysis for fracture estimation. The evaluation technology has been successfully applied in the Tabei and Tazhong areas, and commercial production of oil and gas has been achieved. We show the application of this technology in the Lunguxi area in North Tarim in this paper.

An optimized industry processing technology of peanut tofu and the novel prediction model for suitable peanut varieties

Peanut protein is easily digested and absorbed by the human body,and peanut tofu does not contain flatulence factors and beany flour.However,at present,there is no industrial preparation process of peanut tofu,whereas the quality of tofu prepared by different peanut varieties is quite different.This study established an industrial feasible production process of peanut tofu and optimized the key process that regulates its quality.Compared with the existing method,the production time is reduced by 53.80%,therefore the daily production output is increased by 183.33%.The chemical properties of 26 peanut varieties and the quality characteristics of tofu prepared from these 26 varieties were determined.The peanut varieties were classified based on the quality characteristics of tofu using the hierarchical cluster analysis(HCA)method,out of which 7 varieties were screened out which were suitable for preparing peanut tofu.An evaluation standard was founded based on peanut tofu qualities.Six chemical trait indexes were correlated with peanut tofu qualities(P<0.05).A logistic regressive model was developed to predict suitable peanut varieties and this prediction model was verified.This study may help broaden the peanut protein utilization,and provide guidance for breeding experts to select certain varieties for product specific cultivation of peanut.

Shale gas production evaluation framework based on data-driven models

Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.

Chemical kinetics evaluation and its application of natural gas generation derived from the Yacheng Formation in the deep-water area of the Qiongdongnan Basin,China

The natural gas generation process is simulated by heating source rocks of the Yacheng Formation, including the onshore-offshore mudstone and coal with kerogens of Type II2-III in the Qiongdongnan Basin. The aim is to quantify the natural gas generation from the Yacheng Formation and to evaluate the geological prediction and kinetic parameters using an optimization procedure based on the basin modeling of the shallow-water area. For this, the hydrocarbons produced have been grouped into four classes（C1, C2, C3 and C4-6）. The results show that the onset temperature of methane generation is predicted to occur at 110℃ during the thermal history of sediments since 5.3 Ma by using data extrapolation. The hydrocarbon potential for ethane, propane and heavy gaseous hydrocarbons（C4-6） is found to be almost exhausted at geological temperature of 200℃ when the transformation ratio（TR） is over 0.8, but for which methane is determined to be about 0.5 in the shallow-water area. In contrast, the end temperature of the methane generation in the deep-water area was over 300℃ with a TR over 0.8. It plays an important role in the natural gas exploration of the deep-water basin and other basins in the broad ocean areas of China. Therefore, the natural gas exploration for the deep-water area in the Qiongdongnan Basin shall first aim at the structural traps in the Ledong, Lingshui and Beijiao sags, and in the forward direction of the structure around the sags, and then gradually develop toward the non-structural trap in the deep-water area basin of the broad ocean areas of China.

Evaluation of Latest TMPA and CMORPH Precipitation Products with Independent Rain Gauge Observation Networks over High-latitude and Low-latitude Basins in China

The Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） and National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） morphing technique （CMORPH） are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products （i.e., Version-7 real-time TMPA （T-rt） and gauge-adjusted TMPA （T-adj）, and Version- 1.0 real-time CMORPH （C-rt） and Version-l.0 gauge-adjusted CMORPH （C-adj）） are evaluated and intercompared by using independent rain gauge observations for a 12-year （2000--2011） period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite pre- cipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.