Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting.

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

Environmental risk evaluation for radionuclide transport through natural barriers of nuclear waste disposal with multi-scale streamline approaches.

Natural barriers, encompassing stable geological formations that serve as the final bastion against radionuclide transport, are paramount in mitigating the long-term contamination risks associated with the nuclear waste disposal. Therefore, it is important to simulate and predict the processes and spatial-temporal distributions of radionuclide transport within these barriers. However, accurately predicting radionuclide transport on the field scale is challenging due to uncertainties associated with parameter scaling. This study develops an integrated evaluation framework that combines upscaled parameters, streamline transport models, and response surface techniques to systematically assess environmental risk metrics and parameter uncertainties across different scales. Initially, upscaling methods are established to estimate the prior interval of critical transport parameters at the field scale, and streamline models are derived by considering the radionuclides transport with a variety of physicochemical mechanisms and geological characterizations in natural barriers. To assess uncertainty ranges of the risk metrics related to upscaled parameters, uncertainty quantification is performed on the ground of 5000 Monte Carlo simulations. The results indicate that the upscaled dispersivity of fractured media (αLf) has a relatively high sensitivity ranking on release dose for all nuclides, and upscaled matrix sorption coefficient (Kd) of Pu-242 strongly affects breakthrough time and release dose of Pu-242. Facilitated by robust response surface with the lowest R2 of 0.89, it is shown that the release doses of Pu-242 and Pb-210 increase under conditions of low Kd and αLf, respectively. Furthermore, statistical analysis reveals that employing limited laboratory-scale parameters results in narrower confidence intervals for risk metrics, while upscaling methods better account for the highly heterogeneous properties of large-scale field conditions. The developed risk evaluation framework provides valuable insights for utilizing upscaled parameters and modeling radionuclide transport within natural barriers under various scenarios.

Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management.

Traditional mine water inflow prediction is characterized by a high degree of uncertainty in model parameters and complex mechanisms involved in the water inflow process. Data-driven models play a key role in predicting inflow mechanisms without considering physical changes. However, the existing models are limited by nonlinearity and non-stationarity. Thus, the principal objective of this study was to propose two robust models, the DIFF-TCN model and the DIFF-LSTM model, for predicting the average water inflow per day. The models consist of three methods, namely Difference Method (DIFF), Temporal Convolutional Neural Network (TCN), and Long Short-Term Memory Neural Network (LSTM). When applied to the Tingnan Coal Mine, Shanxi Province, China, the DIFF-TCN performs better in predicting the average daily water inflow, the model has a MAE of 5.88 m3/h, RMSE of 6.85 m3/h and R2 of 0.96 in the test stage of the water inflow event. Comparison with the other deep learning models (with similar complex structures) and traditional time series model shows the superiority of our proposed DIFF-TCN model. The SHAP value is used to explain the contribution of each model input to the predicted values, and it indicates that the historical time of water inflow data are the most important input, and the advance distance and the groundwater level data also contribute to the model predictions, but groundwater level data for some periods in the past may have a detrimental effect on the model. The findings of this study can provide better understanding about potential of robust deep learning models for smart hydrological forecasting, and they can also provide technical guidance for mining safety production and protection of water resources and water environment around the mining area.

Investigating the influence of bentonite colloids on strontium sorption in granite under various hydrogeochemical conditions.

The disposal of high-level radioactive waste in deep geological repositories is a critical environmental issue. The presence of bentonite colloids generated in the engineering barrier can significantly impact the transport of radionuclides, but their effect on radionuclide sorption in granite remains poorly understood. This study aimed to investigate the sorption characteristics of strontium (Sr) on granite as well as on the coexistence system of granite and colloids under various hydrogeochemical conditions, through batch experiments. Fourier transform infrared spectroscopy was employed to analyze the sorption forms of Sr on granite before and after sorption. Several hydrogeochemical factors were examined, including contact time, pH, ionic strength, coexisting ions, and bentonite and humic acid colloid concentration. Among these factors, the concentration of bentonite colloids exhibited a significant effect on Sr sorption. Within a specific range of colloid concentration, the sorption of Sr on the solid system increased linearly with the bentonite colloid concentration. pH and ionic strength were also found to play crucial roles in the sorption process. At low pH, Sr sorption primarily occurred through the outer sphere's surface complexation and Na+/H+ ion exchange. However, at high pH, inner sphere surface complexation dominated the process. As the ionic strength increased, electrostatic repulsion gradually increased, resulting in fewer binding sites for particle aggregation and Sr sorption on bentonite colloids. The results also indicate that with increasing pH, the predominant forms of Sr in the solution transitioned from SrHCO3+ and SrCl+ to SrCO3 and SrCl+. This was mainly due to the ion exchange of Ca2+/Mg2+ in plagioclase and biotite, forming SrCO3 precipitation. These findings provide valuable insights into the transport behavior of radionuclides in the subsurface environment of the repository and highlight the importance of considering bentonite colloids and other hydrogeochemical factors when assessing the environmental impact of high-level radioactive waste disposal.

Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory.

A sustainable environment by decreasing fossil fuel utilization and anthropogenic greenhouse gases is a globally main goal due to climate change and serious air pollution. Carbon dioxide (CO2) is a heat-trapping (greenhouse) that is released into the earth's atmosphere from natural processes, such as volcanic respiration and eruptions, as well as human activities, such as burning fossil fuels and deforestation. Due to this fact, underground carbon storage (UCS) is a promising technology to cut carbon emissions. However, there are some barriers to prevent UCS from applying globally. One of them is evaluating the feasibility of storage projects. Thus, the prediction accuracy of CO2 storage efficiencies may promote the attention of the community for UCS. In this study, we utilize the recent advances of swarm intelligence to develop a hybrid algorithm called AOSMA, employed to train the long short-term memory (LSTM). The developed swarm intelligence method (AOSMA) is an enhanced Aquila optimizer (AO) using the search mechanism of the slime mould algorithm (SMA). It is used to boost the prediction capability of the LSTM by optimizing its parameters. We considered two CO2 trapping indices, called residual trapping index (RTI) and solubility trapping index (STI). The evaluation experiments have shown that the AOSMA achieved significant results compared to the original AO and SMA and several swarm intelligence and optimization algorithms. The developed smart tools could use as a game changer to provide fast and accurate storage efficiency for projects that have similar parameters falling within the range of the database.

Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage.

The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero. The data were collected from 229 datasets of contact angle in three states of shale/oil/brine, shale/CO2/brine, and shale/CH4/brine systems. Five algorithms were used to tune MLP, while three optimization algorithms were used to optimize the RBFNN computing framework. The results indicate that the RBFNN-MVO model achieved the best predictive accuracy, with a root mean square error (RMSE) value of 0.113 and an R2 of 0.999993. The sensitivity analysis showed that theta zero, TOC, pressure, temperature, and salinity were the most sensitive features. This research demonstrates the effectiveness of RBFNN-MVO model in evaluating shale wettability for CCUS initiatives and cleaner production.

Upscaling dispersivity for conservative solute transport in naturally fractured media.

Physical heterogeneities are prevalent features of fracture systems and significantly impact transport processes in aquifers across different spatiotemporal scales. Upscaling solute transport parameter is an effective way of quantifying parameter variability in heterogeneous aquifers including fractured media. This paper develops conceptual models for upscaling conservative transport parameters in fracture media. The focus is on upscaling dispersivity. Lagrangian-based transport model (LBTM) for dispersivity upscaling are derived for the solute transport in two-dimensional fractures surrounded by an impermeable matrix. The LBTM is validated against the random walk particle tracking (RWPT) model, which enables highly efficient and accurate predictions of conservative solute transport. The results show that the derived scale-dependent analytical expressions are in excellent agreement with RWPT model results. In addition, LBTM results are also compared to experimental results from the observed breakthrough curve of a conservative solute transport through a single natural fracture within a granite core. Comparing results from the LBTM and transport experiment shows that LBTM based estimated dispersivity is 10.55% higher than the measured value. Errors introduced by the experiments, the conceptual assumptions in deriving models, and the heterogeneities of fracture apertures not fully sampled by measuring instruments are main factor for such discrepancy. The sensitivity analysis indicates that the longitudinal and transverse dispersivities are positively related to the integral scale and the variance of the log-fracture aperture. The longitudinal dispersivity is strongly contolled by the variance of the log-fracture aperture. The LBTM may be useful for directly predicting solute transports, requiring only the acquisition of fractured geostatistical data. This work provides a better understanding of transport processes in fractured media which ultimately control water quality across scales.