Modeling the correlations of crude oil properties based on sensitivity based linear learning method

This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crudeoil systems using sensitivity based linear learning method (SBLLM). PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties, such as bubble-point pressure and oil formation volume factor, is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially their instability and inconsistency during predictions. In this paper, a sensitivitybasedlinearlearningmethod (SBLLM) prediction model for PVT properties is presented using three distinct databases while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. In the formulation used, sensitivity analysis coupled with a linear training algorithm for each of the two layers is employed which ensures that the learning curve stabilizes soon and behaves homogenously throughout the entire process operation. In this way, the model will be able to adequately model PVT properties faster with high stability and consistency. Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling

Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained.Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.0

Crude oil risk forecasting : new evidence from multiscale analysis approach

Fluctuations in the crude oil price allied to risk have increased significantly over the last decade frequently varying at different risk levels. Although existing models partially predict such variations, so far, they have been unable to predict oil prices accurately in this highly volatile market. The development of an effective, predictive model has therefore become a prime objective of research in this field. Our approach, albeit based in part on previous research, develops an original methodology, in that we have created a risk forecasting model with the ability to predict oil price fluctuations caused by changes in both fundamental and transient risk factors. We achieve this by disintegrating the multi-scale risk-structure of the crude oil market using Variational Mode Decomposition. Normal and transient risk factors are then extracted from the crude oil price using Variational Mode Decomposition and modelled separately using the Quantile Regression Neural Network (QRNN) model. Both risk factors are integrated and ensembled to produce the risk estimates. We then apply our proposed risk forecasting model to predicting future downside risk level in three major crude oil markets, namely the West Taxes Intermediate (WTI), the Brent Market, and the OPEC market. The results demonstrate that our model has the ability to capture downside risk estimates with significantly improved precision, thus reducing estimation errors and increasing forecasting reliability

Intelligent time-successive production modeling

A new framework is presented that uses production data history in order to build a field-wide performance prediction model. In this work artificial intelligence techniques and data driven modeling are utilized to perform a future production prediction for both synthetic and real field cases.;Production history is paired with geological information from the field to build large dataset containing the spatio-temporal dependencies amongst different wells. These spatio-temporal dependencies are addressed by information from Closest Offset Wells (COWs). This information includes geological characteristics (Spatial) and dynamic production data (Temporal) of all COWs.;Upon creation of the dataset, this framework calls for development of a series of single layer neural network, trained by back propagation algorithm. These networks are then fused together to form the Intelligent Time-Successive Production Modeling (ITSPM). Using only well log information along with production history of existing wells, this technique can provide performance predictions for new wells and initial hydrocarbon in place (IHIP) using a volumetric-geostatical method.;A synthetic oil reservoir is built and simulated using a commercial reservoir numerical simulation package. Production and well log data are extracted and converted to an all-inclusive dataset. Following the dataset generation several neural networks are trained and verified to predict different stages of production. ITSPM method is utilized to estimate the production profile for nine new wells in the reservoir. ITSPM is also applied to data from a real field. The field that is giant oil field in the Middle East includes more than 200 wells with forty years of production history. ITSPM\u27s production predictions of the four newest wells in this reservoir are compared to real production data

Data Analysis and Neuro-Fuzzy Technique for EOR Screening : Application in Angolan Oilfields

This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use the data and publish this manuscriptPeer reviewedPublisher PD

Application of statistical and neural network model for oil palm yield study

This thesis presents an exploratory study on modelling of oil palm (OP) yield using statistical and artificial neural network approach. Even though Malaysia is one of the largest producers of palm oil, research on modelling of OP yield is still at its infancy. This study began by exploring the commonly used statistical models for plant growth such as nonlinear growth model, multiple linear regression models and robust M regression model. Data used were OP yield growth data, foliar composition data and fertiliser treatments data, collected from seven stations in the inland and coastal areas provided by Malaysian Palm Oil Board (MPOB). Twelve nonlinear growth models were used. Initial study shows that logistic growth model gave the best fit for modelling OP yield. This study then explores the causality relationship between OP yield and foliar composition and the effect of nutrient balance ratio to OP yield. In improving the model, this study explores the use of neural network. The architecture of the neural network such as the combination activation functions, the learning rate, the number of hidden nodes, the momentum terms, the number of runs and outliers data on the neural network’s performance were also studied. Comparative studies between various models were carried out. The response surface analysis was used to determine the optimum combination of fertiliser in order to maximise OP yield. Saddle points occurred in the analysis and ridge analysis technique was used to overcome the saddle point problem with several alternative combinations fertiliser levels considered. Finally, profit analysis was performed to select and identify the fertiliser combination that may generate maximum yiel

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain