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Multiphase flow through both vertical and horizontal tubulars is getting higher interest in the oil and gas industry. Prediction of wellhead pressure through vertical wells is a very critical point that has a great influence on different applications. In this research, an artificial neural network with backpropagation technique (ANN-BP) was used to predict the wellhead pressure (WHP) for multiphase flow for vertical well systems. This permits the calculation of the pressure drop across the vertical well section by knowing the bottom hole flowing pressure (BHP). More than 150 data sets from different wells in the Middle East with different conditions were used to build the model. About 80% of the data were used to train the model while the rest unseen 20% were used to test and validate the model. The network structure, including the training function, the transfer function, the number of hidden layers, and the number of neurons in each layer, was highly optimized by trying different combinations of each parameter. The developed ANN model yielded high accuracy in predicting the WHP with an average absolute percentage error (AAPE) for both training and testing which are 0.61% and 1.13%, respectively. The optimized model comprised a single hidden layer with 20 neurons activated with the transfer function “tansig.” The correlation coefficient between the actual and predicted values for both training and testing was 0.98. A new empirical equation was then developed to mimic the developed ANN model by extracting the network weights and biases. The developed ANN-based correlation outweighs the previously established correlations in the literature upon comparison using unseen dataset.

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (YP), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

Accurate hydrocarbon reserve estimation is a crucial step for successful field development. Unlike for conventional reservoirs, however, reserve estimation for unconventional reservoirs is challenging due to the multiscale transport and multiphysics storage mechanisms involved. In this paper, we investigate the applicability and the limitations of Langmuir adsorption isotherm for the major unconventional gas resources, namely, shale-gas and coalbed methane (CBM) reservoirs, respectively. In general, reserve estimation methods for both shale-gas and CBM rely on Langmuir isotherm to model the sorbed gas capacity. Thus, we provide a detailed discussion on the characteristics of unconventional reserves and elucidate the applicability of the Langmuir model for estimating gas storage volumes. To add to the discourse on storage capacity modeling, molecular simulation studies of organic materials (kerogen) with various degree of heterogeneity were conducted. The adsorption behavior of multicomponent mixtures was also investigated. Simulations suggest that increased heterogeneity of the organic constituents and the presence of more than one component curtail the predictive power of the Langmuir framework. Furthermore, we observed that kerogen storage capacity is not only governed by its chemical composition but also by the particular kerogen type. Observed discrepancies with respect to reserve estimates for different evaluation models hint at a lack of understanding the underlying dynamics of unconventional reservoir gas storage phenomena.

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young&rsquo, s modulus and Poisson&rsquo, s ratio. Accurate determination of the Poisson&rsquo, s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson&rsquo, s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson&rsquo, s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson&rsquo, s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson&rsquo, s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson&rsquo, s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson&rsquo, s ratio without the need to run the ANN model.

Cement Evaluation Logs are generally used to evaluate the quality of the cement sheath behind the casing or liner before a well test or production operation is performed in the well. In many countries, regulatory authorities require that cement evaluation logs be done in every well after cementing operations to investigate the presence and quality of the cement sheath. The main objective of the cement evaluation process is to evaluate the hydraulic isolation of the cement sheath. The main principle of cement bond log (CBL) tools is to send an acoustic signal inside the casing and measuring the acoustic impedance of the cement between the casing and the formation. The interpretation and processing of these measurements are sometimes complicated and difficult to understand because CBL can be affected by the downhole conditions and the input parameters for processing. Therefore, it is important to understand how the log is processed before planning for any remedial action based on the apparently poor cement quality obtained from the log. Also, there are some challenges such as lightweight cement, the presence of micro-annulus, and eccentricity of the tool itself may affect the measurement and the relating remedial action to solve the problem. Conventional cement evaluation tools are useful in most scenarios, but in more challenging operations, there is a pressing need for more advanced cement evaluation techniques to increase the confidence of the measurements for the evaluation of wellbore integrity. This paper demonstrates ultrasonic cement evaluation tools principles and examples for evaluating the quality of the cement bond. Also, the most common challenges and conditions which affect the sonic signal and lead to falsely indication of effective cement integrity. The processed case studies highlight the confidence of the cement isolation identified using these tools and evaluation techniques and their impact on the upcoming remedy actions.