Abstract Surface network production flow in the oil and gas industry is a complex process influenced by fluid properties, pipeline configurations, and operational conditions. Traditionally, this estimation is performed using nodal analysis or empirical correlations, both of which rely on physical assumptions and theoretical models. However, these conventional methods often introduce significant assumptions and simplifications that may not fully capture the complexities of real-world production behavior. In this work, a Data-Driven approach was used, where machine-learning models analyze historical production data to learn patterns without relying on physics-based assumptions. Trends and relationships are identified solely from historical data, eliminating the need for complex physical equations or empirical formulas. The objective is to use pure data analytics to build an accurate oil-production prediction model. This study explores the application of machine-learning techniques for oil and gas production forecasting, integrating exploratory data analysis (EDA) and predictive modeling using historical production data from different oil platforms. A Python-Dash dashboard was developed for historical monitoring of key production metrics, featuring dynamic visualization and seamless data integration. Prediction of oil, gas, and water rates was carried out using three regression models, Random Forest, XGBoost, and Support Vector Regression (SVR) with data preprocessing ensuring numerical consistency and null-free values for effective training. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² score, with XGBoost identified as the best-performing model. Predictions on the validation dataset were visualized through line plots, highlighting production trends and fluctuations. The cleaned and forecasted data were exported for further analysis and decision-making. This approach demonstrates the potential of machine learning in optimizing oilfield operations and enhancing resource management through accurate production forecasting.
8Comparative analysis of machine learning models for rainfall prediction
Comparative analysis of machine learning models for rainfall prediction
1Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy
The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.
Read moreMACHINE LEARNING IN ENERGY EFFICIENCY: COMPARISON OF ENERGY ESTIMATION MODELS
In this study, the functionality of machine learning models was tried to be determined in order to estimate the energy consumption needed in industrial production. In this context, Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors and Support Vector Machine algorithms were compared and evaluated in terms of usability and performance values. In the study, five different machine learning models were compared to estimate energy consumption. In order to make energy consumption estimates, historical production data, energy consumption data and other relevant parameters were used as input data. Data was obtained from UCI data repository, an open-source platform. The machine learning process structured as 80/20 training/testing was adapted to the form where the models can perform energy efficiency analysis with comprehensive data parameters. Error metrics such as coefficient of determination (R²), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) were used to evaluate the performance of the models. According to the findings, the Random Forest model used in the study provided a higher accuracy rate compared to other models, and the R² value was obtained as 0.9989. This result reveals that machine learning models can be used as effective tools in estimating energy consumption and that these tools can turn into a strategic advantage for businesses, considering the importance of energy in production. The research provides significant contributions to literature by revealing that machine learning technology can be an important tool in energy consumption and, moreover, by comparing the performance of different models in energy estimation.
Read more29Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study
An accurate energy consumption prediction becomes crucial with increasing electric vehicle usage for effective power grid management. This research examined the performance of eleven machine learning models for this purpose: Ridge Regression, Lasso Regression, K-Nearest Neighbors, Gradient Boosting, Support Vector Regression, Multi-Layer Perceptron, XGBoost, CatBoost, LightGBM, Gaussian Processes for Regression(GPR) and Extra Trees Regressor, considering real historical data from Colorado. The models were evaluated using different metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), R², Root Mean Squared Error(RMSE) and Normalized Root Mean Squared Error(NRMSE), with visual analyses through scatter plots and time series plots. The best model observed was the Extra Trees Regressor, which had an MAE of 0.5888, an MSE of 3.2683, R² value of 0.9592, RMSE of 1.8078 and NRMSE of 0.020. Gradient Boosting and KNN also returned good results, although they were slightly more dispersed. Nevertheless, while non-linear models like MLP, XGBoost, CatBoost, LightGBM and linear models such as Ridge and Lasso Regression offer valuable insights, they exhibit shortcomings in estimating energy, especially at extreme levels, highlighting limitations in capturing complex non-linear interactions. This study focuses on their applicability to energy projections to demonstrate how well ensemble and non-linear models may capture intricate patterns in time series. These cutting-edge machine learning techniques might greatly enhance energy demand predictions.
Read more57Monte Carlo simulation-based traffic speed forecasting using historical big data
Monte Carlo simulation-based traffic speed forecasting using historical big data
1Research on Stock Price Prediction Model Based on LSTM
Stock prices are a non-linear, long-term series of data. The research topic of this paper is to use the Long Short-Term Memory Network (LSTM) model to analyze the historical stock price data of BYD (BYD) to predict the future stock price trend, and to provide intelligent reference for investors by evaluating the model performance, aiming to improve the accuracy of stock price prediction. The accuracy and consistency of stock price prediction can be increased by using this method, which successfully takes advantage of the time characteristics in historical data. After explaining the idea and architecture of LSTM, this article creates a stock price prediction model based on it, using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as evaluation metrics. At the same time, network structures with different layers were designed to verify the effectiveness of the model, and the experimental results were shown. The advantages and limitations of the model are highlighted, and the feasibility of deep learning in the stock financial market and the credibility and advantages of strategic decision-making are demonstrated.
Read more2Improving Recovery in the Yates Field Using Dynamic Feedback Loop based on Physics-Informed Artificial Intelligence
We present a robust control system and methodology for physics-informed artificial intelligence (PAI) used to optimize and improve oil recovery, demonstrated in the Yates Field operated by the Kinder Morgan CO2 company. The system consists of a robust control system (referred to as Dynamic Feedback Loop, or DFL) equipped with novel hydrocarbon sensors that measure oil concentration and other parameters continuously and simultaneously on a set of producing wells. The goal of this system is to optimize operational parameters (e.g. choke valve settings, injection rates) to reach specific target metrics of production (e.g. maximizing produced oil while minimizing produced gas).The key element of our approach is the use of a multi-layer artificial neural network (deep neural network, or DNN, to be specific) that extracts physics-based parameters from the real-time measurements and predicts relevant parameters of the DFL control system. DNNs are prone to overfitting in training, making them ineffective in unfamiliar or challenging situations outside the training dataset. To overcome this problem, we have developed a physics-informed robust neural network technique, where the reservoir physics and sensor data are used to train DNN representations of the key physical parameters. Typically, only simplified physical models are developed using available geostatic or historical production data. Also, due to the dynamic nature of these systems, the accuracy of the models often changes over time. To improve predictive capability of the model, we combine the DNN with the system-theoretic robust control concepts based on physics with a model uncertainty formulation. The concept was first validated using a combination of simulations, isolated sensor data and analyses based on sets of historic production data.A study using historic production data on Kinder Morgan's Yates Field Unit (YFU) 4045 Pilot (3 producing wells) indicates application of the DFL system results in an increase in cumulative production of up to 35% per year, compared to what is obtained through a traditional (fixed-point) control system. Currently, the DFL is being field-tested on a different set of wells in the Yates Field, instrumented with the novel hydrocarbon sensors that generate continuous and simultaneous production data.
Read more9Integrating Petrophysical, Hydrofracture, and Historical Production Data with Self-Attention-Based Deep Learning for Shale Oil Production Prediction
As the energy industry increasingly turns to unconventional shale reservoirs to meet global demands, the development of advanced predictive models for shale oil production has become imperative. The inherent complexity of shale formations, coupled with the intricacies of hydraulic fracturing, poses significant challenges to efficient resource extraction. Our study leverages a substantial data set from the Ordos Basin to develop an advanced predictive model, integrating 18 parameters that blend static petrophysical attributes and dynamic factors, including hydraulic fracturing parameters and real-time pump pressure data. This holistic approach enables our self-attention (SA) model to accurately forecast future production rates by processing the complex interplay between reservoir characteristics and operational inputs. In testing across three wells, the model achieved average accuracies of 99.28% for daily oil production (DOP) and 99.25% for daily liquid production (DLP) over 20 days, surpassing traditional long short-term memory (LSTM) and gated recurrent unit (GRU) models, proving its efficacy in fractured well production forecasting. Furthermore, using the initial 30 days of production data as input, the model demonstrated its capability to predict DOP and DLP over a one-year period, achieving prediction accuracies of 96.2% for DOP and 99.6% for DLP rates. Our model’s profound implications for the shale industry include establishing a quantifiable link between key factors and production forecasts, guiding the optimization of controllable aspects, and serving as a decision-support tool for more efficient and cost-effective oil recovery.
Read more59Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria
Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria
Read more6Data Mining Methodologies Enhance Probabilistic Well Forecasting
Decline Curve Analysis (DCA) is one of the most common tools to predict oil and gas well performance and determine an estimated ultimate recovery based on historical production data. Probabilistic approaches have evolved to provide a measure of uncertainty in such estimates. However, engineers have held onto the belief that such quantification of uncertainty is largely subjective. Consequently there is an innate reluctance to adopt probabilistic methodologies owing to the assumption of prior knowledge of the relevant parameters and reservoir properties distributions. The objective of this paper is to explicate the development of an improved probabilistic approach to estimate reserves and well performance based on historical production data. This methodology precludes the assumption of prior distributions by adopting a bootstrap workflow that is implemented to construct probabilistic estimates with specified confidence intervals from historical production data. It is a statistical approach to assess the uncertainty of estimates objectively, removing the subjective nature of prior assumptions. We shall discuss an automated selection criteria workflow for time series and forecast periods resulting in more robust and accurate DCA. The methodology1 abides by a "more rigorous model-based bootstrap algorithm" that encapsulates the appropriate steps to preserve the inherent characteristics of a time series data set that show an overall decline trend. The paper also explores other potential advanced analytical techniques to gather knowledge from historical upstream data so as to identify wells within an asset that require some remediation based on real-time performance observations. Cluster analysis could improve engineers’ convenience in analyzing wells by separating them into groups based on decline curve shapes (patterns) and other reservoir properties. We shall define some processes that implement soft computing techniques to aggregate the DCA results with disparate upstream data via a data driven methodology. Pattern recognition and classification are important steps that underpin data mining, and we shall explicate a suite of workflows to enable a well optimization solution.
Read more21Direct Selection of Stochastic Model Realizations Constrained to Historical Data
A new methodology for stochastic reservoir modelling constrained to well and historical data has been developed and successfully applied to synthetic case studies. Multiple well historical data and prior geostatistical information can be integrated using a Bayesian inversion technique and an efficient optimization algorithm based on the gradient method. This inversion procedure allows direct selection of particular constrained model realizations within a confidence domain of the parameter space. The gradient method, implemented in an industrial numerical reservoir simulator within the fully implicit scheme, provides the sensitivities of production results with respect to the model parameters. These parameters can be, for example, the petrophysical properties over given reservoir zones or the well productivity indexes. Recently, a user-defined parameterization technique of a heterogeneous permeability distribution was introduced in order to locally modify the mean value of an initial stochastic distribution. An efficient quadratically convergent optimization algorithm is combined with the Bayesian formalism to directly obtain a constrained model from an initial realization. Greater efficiency of this procedure is reached by using the gradient results. A priori information on the parameters is introduced through Gaussian or lognormal probability density functions. A maximum likelihood model is obtained after history matching, as well as a posteriori information on the parameters with reduced uncertainties. After history matching, new model realizations can be selected, inside a confidence domain defined by an iso-probability criterion, in order to quantify production forecast uncertainties. An optimization procedure is used to obtain particular model realizations, which correspond to extreme values of a given production forecasting criterion. A successful application on a synthetic case study is described at the end of this paper. The petrophysical properties and the activity coefficient of an analytical aquifer are simultaneously constrained to match the historical data recorded for 7 wells. A parameterization technique is used in order to constrain the mean of a stochastic distribution of petrophysical properties. Introduction Our aim is to constrain geostatistical models to both well data and historical data. The proposed method is based on the Bayesian inversion technique, whose formalism is well-suited for integrating prior knowledge on the geological model and the dynamic data. Moreover, this formalism positions the inverse problem in a stochastic context, allowing uncertainty quantification on production forecasts. The main difficulty encountered in constraining geostatistical models is the cost in terms of CPU time spent to obtain a constrained realization and the amount of realizations needed to quantify uncertainties on production forecasts. The dimension of the inverse problem corresponds, theoretically, to the number of grid blocks multiplied by the number of petrophysical properties to be identified. A reduction of the dimension of the inverse problem is therefore necessary to preserve the feasibility of such methods. A parameterization of the distribution of the petrophysical properties can be used in order to reach this goal. The principle of the proposed method is to define the distribution of petrophysical properties as a function of parameters in a reduced space and to constrain these parameters to the production data. This paper proposes a parameterization technique, called pilot point method, which consists in constraining the initial model to several master points, located on a coarse grid. Each master point locally constrains the mean of an initial model realization. The gradient method can be used to improve the efficiency of the inversion procedure. The sensitivities of the simulations results with respect to the master point values can be used by an optimization algorithm. P. 155^
Read moreAtlas of Appalachian Basin Oil and Gas Plays: The Oriskany Sandstone Play--a Preliminary Example
The assessment of the nation's undiscovered petroleum resources by the US Geological Survey uses the identification of geologically defined plays and the estimation of the size distribution and number of undiscovered fields in each play from historical production data. The Appalachian basin contains six major oil and gas plays, but in contrast to more recently drilled basins, the plays of the mature Appalachian basin lack a complete geologic and production database. Data such as reservoir porosity, permeability, thickness, saturation, drilling depth, and historical production are either unknown, scattered in the literature, deeply buried in company and government files, or exist in incomplete regional government and private commercial data files. In an effort to increase the database and to improve the assessment of the basin, an informal atlas was initiated to test the feasibility of acquiring additional data and the utility of these data in improving the assessment. Critical to the assessment procedure and noticeably incomplete in the basin are the historical cumulative production data necessary for analyzing the distribution of the number of fields and field sizes for the individual plays. Of the Appalachian basin plays, the Oriskany Sandstone play was initially selected and encouraging amount of previously unidentified productionmore » data is available. Achieving the maximum amount of the quality of data for each play in the atlas will require the cooperation of those working in the Appalachian basin.« less
Read more17A novel deep learning approach for investigating liquid fuel injection in combustion system
The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system.
Read moreAccurate Photovoltaic Power Prediction Using Machine Learning and Deep Learning: A Comparative Study Across Multiple Locations
This work evaluates machine learning (ML) and deep learning (DL) models for Photovoltaic (PV) power output prediction based on two real-world datasets. Eight models – Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Decision Tree (DT), one Dimensional Convolutional Neural Network (1DCNN) and Artificial Neural Network (ANN) – were evaluated using Root Mean Squared Error (RMSE), R-squared (R²), Mean Absolute Error (MAE), and Mean Squared Error (MSE). For Dataset1 (region of Cacak) 1DCNN achieved the optimal performance, with the reduced errors and the largest R² values at three spatial locations, and it was closely followed by RFR and GBR. Likewise, in Dataset2 (region of Kraljevo), 1DCNN, ANN, and RFR give the best results. Conventional models, such as LR and DT, performed poorly in both data tribes. The results highlight the ability of state-of-the-art ensemble and DL models in learning nonlinear patterns in solar energy data and thus, the importance of choosing the right prediction tools to provide accurate and reliable PV power forecasts.
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14A Framework-Based Wind Forecasting to Assess Wind Potential with Improved Grey Wolf Optimization and Support Vector Regression
Wind energy is one of the most promising alternates of fossil fuels because of its abundant availability, low cost, and pollution-free attributes. Wind potential estimation, wind forecasting, and effective wind-energy management are the critical factors in planning and managing wind farms connected to wind-pooling substations. Hence, this study proposes a hybrid framework-based approach for wind-resource estimation and forecasting, namely IGWO-SVR (improved grey wolf optimization method (IGWO)-support vector regression (SVR)) for a real-time power pooling substation. The wind resource assessment and behavioral wind analysis has been carried out with the proposed IGWO-SVR optimization method for hourly, daily, monthly, and annual cases using 40 years of ERA (European Center for Medium-Range Weather Forecast reanalysis) data along with the impact of the El Niño effect. First, wind reassessment is carried out considering the impact of El Niño, wind speed, power, pressure, and temperature of the selected site Radhapuram substation in Tamilnadu, India and reported extensively. In addition, statistical analysis and wind distribution fitting are performed to demonstrate the seasonal effect. Then the proposed model is adopted for wind speed forecasting based on the dataset. From the results, the proposed model offered the best assessment report and predicted the wind behavior with greater accuracy using evaluation metrics, namely root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). For short-term wind speed, power, and El Niño forecasting, IGWO-SVR optimization effectively outperforms other existing models. This method can be adapted effectively in any potential locations for wind resource assessment and forecasting needs for better renewable energy management by power utilities.
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