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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.

Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2&ndash, 3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15&ndash, 20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using M W and M F measurements then extract empirical correlations in a white-box mode to predict these properties based on M W and M F . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.

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.