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Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σv), minimum (σh), and maximum (σH) horizontal stresses. The σh and σH are difficult to determine, whereas the overburden stress can be determined directly from the density logs. The σh and σH can be estimated either from borehole injection tests or theoretical finite elements methods. However, these methods are complex, expensive, or need unavailable tectonic stress data. This study aims to apply different machine learning (ML) techniques, specifically, random forest (RF), functional network (FN), and adaptive neuro-fuzzy inference system (ANFIS), to predict the σh and σH using well-log data. The logging data includes gamma-ray (GR) log, formation bulk density (RHOB) log, compressional (DTC), and shear (DTS) wave transit-time log. A dataset of 2307 points from two wells (Well-1 and Well-2) was used to build the different ML models. The Well-1 data was used in training and testing the models, and the Well-2 data was used to validate the developed models. The obtained results show the capability of the three ML models to predict accurately the σh and σH using the well-log data. Comparing the results of RF, ANFIS, and FN models for minimum horizontal stress prediction showed that ANFIS outperforms the other two models with a correlation coefficient (R) for the validation dataset of 0.96 compared to 0.91 and 0.88 for RF, and FN, respectively. The three models showed similar results for predicting maximum horizontal stress with R values higher than 0.98 and an average absolute percentage error (AAPE) less than 0.3%. a20 index for the actual versus the predicted data showed that the three ML techniques were able to predict the horizontal stresses with a deviation less than 20% from the actual data. For the validation dataset, the RF, ANFIS, and FN models were able to capture all changes in the σh and σH trends with depth and accurately predict the σh and σH values. The outcomes of this study confirm the robust capability of ML to predict σh and σH from readily available logging data with no need for additional costs or site investigation.

Lithology types identification is one of the processes geoscientists rely on to understand the subsurface formations and better evaluate the quality of reservoirs and aquifers. However, direct lithological identification processes usually require more effort and time. Therefore, researchers developed several machine learning models based on well-logging data to avoid challenges associated with direct lithological identification and increase identification accuracy. Nevertheless, high uncertainty and low accuracy are commonly encountered issues due to the heterogeneous nature of lithology types. This work aims to employ decision tree ensemble techniques to predict the lithologies more accurately in time saving and cost-efficient manner, accounting for the uncertainty. This study investigated the real-world well logs dataset from the public Athabasca Oil Sands Database to identify and extract the relevant features. Then, we conducted a thorough training using grid search to optimize the hyperparameters of the ensemble decision tree models. This paper evaluated two ensemble techniques: random forest (RF) and extreme gradient boosting (XGB). We picked metrics such as accuracy, precision, and recall to assess the developed models' performance using 5-fold cross-validation. Finally, we performed a chi-squared test to test our hypothesis of the identical performance of the developed models. The XGB and RF models have 94% and 93% accuracy, respectively. Also, the extreme gradient boost model's weighted average recall and precision of 93% and 93% are only 5% and 4% higher than the RF model. In addition, the chi-squared test resulted in a p-value as low as 0.013, suggesting a low probability of difference in both models' performance. Classification of sand and coal formations is more straightforward than sandy shale and cemented sand. The dataset's low representation of sandy shale and cemented sand can be the reason behind their prediction errors. The developed models can classify the studied field lithologies with an overall accuracy of 94%. In addition, there is no statistically significant evidence of a difference in prediction performance between extreme gradient boost and random forest.

Multistage hydraulic fracturing is a technique to extract hydrocarbon from tight and unconventional reservoirs. Although big advancements occurred in this field, understanding of the created fractures location, size, complexity, and proppant distribution is in its infancy. This study provides the recent advances in the methods and techniques used to diagnose hydraulic fractures in unconventional formations. These techniques include tracer flowback analysis, fiber optics such as distributed temperature sensing (DTS) and distributed acoustic sensing (DAS), tiltmeters, microseismic monitoring, and diagnostic fracture injection tests (DFIT). These techniques are used to estimate the fracture length, height, width, complexity, azimuth, cluster efficiency, fracture spacing between laterals, and proppant distribution. Each technique has its advantages and limitations, while integrating more than one technique in fracture diagnostics might result in synergies, leading to a more informative fracture description. DFIT analysis is critical and subjected to the interpreter’s understanding of the process and the formation properties. Hence, the applications of machine learning in fracture diagnostics and DFIT analysis were discussed. The current study presents an extensive review and comparison between different multistage fracture diagnostic methods, and their applicability is provided. The advantages and the limitations of each technique were highlighted, and the possible areas of future research were suggested.

ABSTRACT: Cement matrix is exposed to several loadings, which can harm its key properties and impede its functions, especially at the early age of forming the cement matrix where its properties are not entirely developed. Nanoclay is a powder material with very fine-grained particles which was used as a secondary additive to enhance the properties of cement. This work evaluates the early-time properties of oil well cement prepared with nanoclay powder and compares it with the properties of the base cement (without nanoclay). Several cement samples were prepared and cured for different times (12, 24, 48 and 72 hours) where the compressive strength, permeability, Poisson’s ratio and Young’s modulus of each sample were examined. The results indicated that the compressive strength of both cement systems increased with the increase of the curing time and the nanoclay-based sample had higher strength than base cement. The permeability of the cement decreased as the time of curing increased for the two cement systems and nanoclay cement had lower permeability. Moreover, the Poisson’s ratio decreased and Young’s modulus increased with curing time for both systems and the addition of nanoclay increased its Poisson’s ratio and reduced its Young’s modulus. 1. INTRODUCTION During the practice of drilling operations, cement slurry is pumped into oil and gas wells for the purpose of sealing the rock formation from the well by cementing the steel casing to the wellbore (Backe et al., 1998). Moreover, cement slurry is also used for controlling a zone where considerable losses of drilling mud are happening and setting a kickoff plug for the wellbore (Nelson and Guillot 2006). The main requirements for long term zonal isolation are the proper placement of the cement, providing low permeability, good mechanical durability and the ability to be adapted during the changing conditions of the wellbore (Zhang et al., 2020).

Unconfined compressive strength (UCS) is a major mechanical parameter of the rock which has an essential role in developing geomechanical models. It can be estimated directly by lab testing of retrieved core samples or from well log data. These methods are very expensive and require huge efforts and time. Therefore, there is a need to develop a new technique for predicting UCS values in real-time. In this study, three artificial intelligence (AI) models were developed using artificial intelligence tools; artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) to predict UCS of the downhole formations while drilling based on real-time recording of the drilling mechanical parameters. These parameters include rate of penetration (ROP), mud pumping rate (GPM), stand-pipe pressure (SPP), rotary speed in revolution per minute (RPM), torque (T), and weight on bit (WOB). A dataset of 1771 points from a Middle Eastern field was used to build the developed models: for training and testing processes. A new UCS correlation was developed based on the optimized AI model. Another set of data (2175 data points unseen by the model) was used to validate the model and the developed UCS correlation. The developed ANN-model outperformed the ANFIS- and SVM-models with a correlation coefficient (R-value) of 0.99 and an average absolute percentage error (AAPE) of 3.48% between the predicted and actual UCS values. The new UCS correlation outperformed the available correlations for UCS prediction and it was able to predict the UCS with AAPE of 4.2% compared to the actual UCS values.

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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.

The least principal stresses of downhole formations include minimum horizontal stress (σmin) and maximum horizontal stress (σmax). σmin and σmax are substantial parameters that significantly affect the design and optimization of the drilling process. These stresses can be estimated using theoretical equations in addition to some field tests, i.e., leak-off test to include the effect of tectonic stress. This approach is associated with many technical and financial issues. Therefore, the objective of this study is to provide a novel machine learning-based solution to estimate these stresses while drilling. First, new models were developed using artificial neural network (ANN) to directly predict σmin and σmax from the drilling data; which are injection rate (Q), standpipe pressure (SPP), weight on bit (WOB), torque (T), and rate of penetration (ROP). Such data are always available while drilling, and hence, no additional cost is required. Actual data from a Middle Eastern field were collected, statistically analyzed, and fed to the models. First, the models’ predictions showed a significant match with the actual stress values with a correlation coefficient (R-value) exceeding 0.90 and a mean absolute average error (MAPE) of 0.75% as a maximum. Second, new empirical equations were generated based on the developed ANN-based models. The new equations were then validated using another unseen dataset from the same field. The predictions had an R-value of 0.98 and 0.93 in addition to MAPE of 0.36% and 0.96% for σmin and σmax models, respectively. The results demonstrated the outperformance of the developed ANN-based equations to estimate the least principal stresses from the drilling data with high accuracy in a timely and economically effective way.

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.

Synthetic well-log generation using artificial intelligence tools is presented as a robust solution when the logging data are not available or partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. It is measured in the field using density log tool either while drilling by logging while drilling technique (LWD) or mostly by wireline logging after the formations are drilled because of the operational limitations during the drilling process. Therefore the objective of this study is to develop a predictive tool for estimating RHOB while drilling using artificial neural networks (ANN) and Adaptive network-based fuzzy interference systems (ANFIS). The proposed models used the drilling mechanical parameters as feeding inputs and the conventional RHOB log-data as an output. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), stand-pipe pressure (SPP) and rotating speed (RPM), are usually measured while drilling and their responses vary with different formations. A dataset of 2400 actual data points obtained from horizontal well in the Middle East is used for building the proposed models. The obtained dataset is divided into 70/30 ratios for training and testing the model respectively. The optimized ANN-based model outperformed the ANFIS-based model with correlation coefficient (R) of 0.95 and average absolute percentage error (AAPE) of 0.72 % between the predicted and the measured RHOB compared to R of 0.93 and AAPE of 0.81 % for the ANFIS-based model. These results demonstrated the reliability of the developed ANN model to predict the RHOB while drilling based on the drilling mechanical parameters. Afterwards, the ANN-based model is validated using unseen data from another well within the same field. The validation process yielded AAPE of 0.5 % between the predicted and the actual RHOB values which confirmed the robustness of the developed model as an effective predictive tool.

Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB.

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.

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.