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Main aspect of this study is to explore the possible use of Machine learning based approaches in the petroleum industry. The fourth industrial revolution is being ushered in by advances in computer power. The oil and gas sector contains a huge amount of produced oil data from wells that can be utilised to create data-driven models which can validate empirical correlations. This validation is vital in the current context because the oil market fluctuates a lot, thus making efficient judgments based on insights supplied by data science and machine learning algorithms are valuable. Artificial Neural Network (ANN), Random Forest Regressor (RF) and Gradient Boosting Regressor (GB) were used in this study to forecast daily oil production using available production parameters. In this study, the production data from Equinor's Volve Field was utilised. The obtained data was filtered, and then it was applied to mentioned algorithms with fine tuned hyperparameters. The data was split into training data (70%) and test data (30%). The model was constructed on training data then it was validated through test data. Seven parameters were chosen on the basis of magnitude of correlation coefficient. The result showed that even with the cases of several shut-ins, the hydrocarbon forecasting method for well 159-F-1C predicted production rates with a coefficient of determination (R2score) value greater or equal to 0.90. For performance analysis, coefficient of determination (R2score), mean squared error (MSE) and mean absolute error (MAE) were used for the selected wells. The outcome showed that prediction of hydrocarbon production for well 15/9-F-1 C ANN model showed the best performance (R2 score = 0.9, MAE = 20.52 and MSE = 1268) and for well 15/9-F-12 RF model shows the best performance (R2 score = 0.98, MAE = 84.95 and MSE = 37356.07). This novel approach could be trained on any dataset to assist in oil forecasting. • If sufficient historical data of any field is available it is possible to construct a model that can predict hydrocarbon production. • Data-driven approaches can predict hydrocarbon production for unconventional resources. • Machine learning algorithms cannot replace the conventional methods, but can assist conventional methods in improving efficiency and accuracy. • Different machine learning algorithms should be trained and evaluated on the dataset for selecting the best algorithm. • There is no one specific model for production prediction as no machine learning algorithm is superior. [ABSTRACT FROM AUTHOR]

Machine learning supports prediction and inference in multivariate and complex datasets where observations are spatially related to one another. Frequently, these datasets depict spatial autocorrelation that violates the assumption of identically and independently distributed data. Overlooking this correlation result in over-optimistic models that fail to account for the geographical configuration of data. Furthermore, although different data split methods account for spatial autocorrelation, these methods are inflexible, and the parameter training and hyperparameter tuning of the machine learning model is set with a different prediction difficulty than the planned real-world use of the model. In other words, it is an unfair training-testing process. We present a novel method that considers spatial autocorrelation and planned real-world use of the spatial prediction model to design a fair train-test split. Demonstrations include two examples of the planned real-world use of the model using a realistic multivariate synthetic dataset and the analysis of 148 wells from an undisclosed Equinor play. First, the workflow applies the semivariogram model of the target to compute the simple kriging variance as a proxy of spatial estimation difficulty based on the spatial data configuration. Second, the workflow employs a modified rejection sampling to generate a test set with similar prediction difficulty as the planned real-world use of the model. Third, we compare 100 test sets' realizations to the model's planned real-world use, using probability distributions and two divergence metrics: the Jensen-Shannon distance and the mean squared error. The analysis ranks the spatial fair train-test split method as the only one to replicate the difficulty (i.e., kriging variance) compared to the validation set approach and spatial cross-validation. Moreover, the proposed method outperforms the validation set approach, yielding a minor mean percentage error when predicting a target feature in an undisclosed Equinor play using a random forest model. The resulting outputs are training and test sets ready for model fit and assessment with any machine learning algorithm. Thus, the proposed workflow offers spatial aware sets ready for predictive machine learning problems with similar estimation difficulty as the planned real-world use of the model and compatible with any spatial data analysis task. • Our data split method handles spatial autocorrelation and imposes prediction fairness. • The sets impose fair algorithms with similar difficulty in all machine learning steps. • Kriging variance is a surrogate of spatial prediction difficulty. • The resulting training and test sets are compatible with any machine learning model. [ABSTRACT FROM AUTHOR]

The identification of lithology while drilling with horizontal well bit is a difficult problem to solve in geosteering. However, due to the existence of "zero length" (the distance between the logging tool and the bit), the lithology at the horizontal well bit cannot be accurately interpreted in real time, which creates a lag in geosteering. Based on logging while drilling (LWD) technology, this paper uses supervised learning in machine learning methods, conventional logging interpretation technology and big data idea in modern computer science, through interdisciplinary theories and methods to research lithology prediction for horizontal well bits in reservoirs. First, a measurement point and vertical reservoir boundary distance (D-MP-VRB) database is built according to different step size categories. Second, based on the D-MP-VRB database, D-MP-VRB prediction models are established using one-versus-one support vector machines (OVO SVMs), random forest (RF), neural networks (NN), and extreme gradient boosting tree (XGBoost) algorithms. To reduce the dimensions of the input data, the feature parameters of the samples are obtained by a correlation analysis of the logging data. The optimal parameter values of each algorithm are determined by grid search and 10-fold cross-validation methods. Finally, the prediction formula of the bit and vertical reservoir boundary distance based on the D-MP-VRB prediction model is established. A case study is performed with data from a sandstone reservoir in Changqing oilfield, Ordos Basin, China. On this basis, the lithology predictions at the bit in real time are carried out by using four models. Considering the principle of model prediction accuracy, through 1320 experiments, only the XGBoost prediction model can be selected, and the step size of the target category is 2 m, however, this model takes the longest time to train. Therefore, the reliable prediction model trained by the sample data of the original training set is used to predict the reservoir information encountered by horizontal well. After the newly drilled reservoir information has accumulated to a certain amount and accurately explained, it is added to the original training set sample data, and the prediction model is retrained to improve the accuracy and adaptability of the model. Based on the prediction results of the XGBoost model and the prediction formula, the distance prediction between the horizontal well bit and the vertical reservoir boundary is realized, real-time lithology correction at the bit is realized, and the adverse effect of the "zero length" on the lithology prediction at the bit is reduced. The research results provide not only a new method for the real-time prediction of lithology in horizontal well bits but also a theoretical basis for the geosteering of oilfield development and valuable information for future research. • A new method for real-time prediction of measurement point and vertical reservoir boundary distance (D-MP-VRB) while drilling is proposed. Real-time prediction models for D-MP-VRB were established and optimized by combining LWD technology with machine learning method based on OVO SVMs, RF, NN and XGBoost algorithms. The simultaneous of efficient drilling and D-MP-VRB prediction is realized. • A prediction formula of the horizontal well bit and vertical reservoir boundary distance based on the D-MP-VRB prediction model is established. The distance prediction between the horizontal well bit and the vertical reservoir boundary is realized, real-time lithology correction at the bit is realized, and the adverse effect of "zero length" on the lithology prediction at the bit is reduced. [ABSTRACT FROM AUTHOR]