Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach

Effects of porous structure on the deformation failure mechanism of cement sheaths for wellbores

The influence and mechanism of porous structure on the deformation failure of cement sheaths under hydraulic pressure is still unclear. To solve this problem, a net slurry cement sheath and a liquid silicon cement sheath were prepared by using a cement material and a liquid silicon suspension. The distributions of the pore radius and spatial location were analyzed using computed tomography scanning and statistics to obtain their probability density distribution functions. Based on the distribution functions, the single-layer and double-layer porous reconstruction models of the net slurry cement sheath and liquid silicon cement sheath were constructed using a FLAC 3D program. A series of numerical simulations were conducted to study the deformation failure of the cement sheaths under in situ stress and hydraulic pressure. The effects of the porous and double-layer structures on the breakdown pressure, plastic failure zone, radial deformation, and stress distribution of the cement sheaths were analyzed. As a result, the mechanisms for the influence of the porous and double-layer structures on the failure mode, failure path, and interaction between the cement sheath and metal casing were revealed. The results of this research provide a theoretical basis for an in-depth understanding of the failure mechanisms of porous cement sheaths.

Real and Imaginary Impedance Prediction of Ni-P Composite Coating for Additive Manufacturing Steel via Multilayer Perceptron

Mathematical models are beneficial in representing a given dataset, especially in engineering applications. Establishing a model can be used to visualise how the model fits the dataset, as was done in this research. The Levenberg–Marquardt model was proposed as a training algorithm and employed in the backpropagation algorithm or multilayer perceptron. The dataset obtained from a previous researcher consists of electrochemical data of uncoated and coated additive manufacturing steel with Ni-P at several testing periods. The model’s performance was determined by regression value (R) and mean square error (MSE). It was found that the R values for non-coated additive manufacturing steel were 0.9999, 1, and 1, while MSE values were 1.14 × 10−6, 2.99 × 10−7, and 5.10 × 10−7 for 0 h, 288 h, and 572 h, respectively. Meanwhile, the R values for the Ni-P coated additive manufacturing steel were 1, 1, 1, while the MSE values were 1.06 × 10−7, 1.15 × 10−8, and 6.59 × 10−8 for 0 h, 288 h, and 572 h, respectively. The high R and low values of MSE emphasise that this training algorithm has shown good accuracy. The proposed training algorithm provides an advantage in processing time due to its ability to approach second-order training speed without having to compute the Hessian Matrix.

Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP.

Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.

Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.