A decision tree model for accurate prediction of sand erosion in elbow geometry.

Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0%, 6.27%, 6.26%, and 5.5%, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time.

Development of a Novel Surfactant-Based Viscoelastic Fluid System as an Alternative Nonpolymeric Fracturing Fluid and Comparative Analysis with Traditional Guar Gum Gel Fluid.

Surfactant-based viscoelastic (SBVE) fluids have recently gained interest from many oil industry researchers due to their polymer-like viscoelastic behaviour and ability to mitigate problems of polymeric fluids by replacing them during various operations. This study investigates an alternative SBVE fluid system for hydraulic fracturing with comparable rheological characteristics to conventional polymeric guar gum fluid. In this study, low and high surfactant concentration SBVE fluid and nanofluid systems were synthesized, optimized, and compared. Cetyltrimethylammonium bromide and counterion inorganic sodium nitrate salt, with and without 1 wt% ZnO nano-dispersion additives, were used; these are entangled wormlike micellar solutions of cationic surfactant. The fluids were divided into the categories of type 1, type 2, type 3, and type 4, and were optimized by comparing the rheological characteristics of different concentration fluids in each category at 25 °C. The authors have reported recently that ZnO NPs can improve the rheological characteristics of fluids with a low surfactant concentration of 0.1 M cetyltrimethylammonium bromide by proposing fluids and nanofluids of type 1 and type 2. In addition, conventional polymeric guar gum gel fluid is prepared in this study and analyzed for its rheological characteristics. The rheology of all SBVE fluids and the guar gum fluid was analyzed using a rotational rheometer at varying shear rate conditions from 0.1 to 500 s-1 under 25 °C, 35 °C, 45 °C, 55 °C, 65 °C, and 75 °C temperature conditions. The comparative analysis section compares the rheology of the optimal SBVE fluids and nanofluids in each category to the rheology of polymeric guar gum fluid for the entire range of shear rates and temperature conditions. The type 3 optimum fluid with high surfactant concentration of 0.2 M cetyltrimethylammonium bromide and 1.2 M sodium nitrate was the best of all the optimum fluids and nanofluids. This fluid shows comparative rheology to guar gum fluid even at elevated shear rate and temperature conditions. The comparison of average viscosity values under a different group of shear rate conditions suggests that the overall optimum SBVE fluid prepared in this study is a potential nonpolymeric viscoelastic fluid candidate for hydraulic fracturing operation that could replace polymeric guar gum fluids.

A Novel ZnO Nanoparticles Enhanced Surfactant Based Viscoelastic Fluid Systems for Fracturing under High Temperature and High Shear Rate Conditions: Synthesis, Rheometric Analysis, and Fluid Model Derivation.

Surfactant-based viscoelastic (SBVE) fluids are innovative nonpolymeric non-newtonian fluid compositions that have recently gained much attention from the oil industry. SBVE can replace traditional polymeric fracturing fluid composition by mitigating problems arising during and after hydraulic fracturing operations are performed. In this study, SBVE fluid systems which are entangled with worm-like micellar solutions of cationic surfactant: cetrimonium bromide or CTAB and counterion inorganic sodium nitrate salt are synthesized. The salt reagent concentration is optimized by comparing the rheological characteristics of different concentration fluids at 25 °C. The study aims to mitigate the primary issue concerning these SBVE fluids: significant drop in viscosity at high temperature and high shear rate (HTHS) conditions. Hence, the authors synthesized a modified viscoelastic fluid system using ZnO nanoparticle (NPs) additives with a hypothesis of getting fluids with improved rheology. The rheology of optimum fluids of both categories: with (0.6 M NaNO3 concentration fluid) and without (0.8 M NaNO3 concentration fluid) ZnO NPs additives were compared for a range of shear rates from 1 to 500 Sec-1 at different temperatures from 25 °C to 75 °C to visualize modifications in viscosity values after the addition of NPs additives. The rheology in terms of viscosity was higher for the fluid with 1% dispersed ZnO NPs additives at all temperatures for the entire range of shear rate values. Additionally, rheological correlation function models were derived for the synthesized fluids using statistical analysis methods. Subsequently, Herschel-Bulkley models were developed for optimum fluids depending on rheological correlation models. In the last section of the study, the pressure-drop estimation method is described using given group equations for laminar flow in a pipe depending on Herschel-Bulkley-model parameters have been identified for optimum fluids are consistency, flow index and yield stress values.

A reservoir bubble point pressure prediction model using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique with trend analysis.

The bubble point pressure (Pb) could be obtained from pressure-volume-temperature (PVT) measurements; nonetheless, these measurements have drawbacks such as time, cost, and difficulties associated with conducting experiments at high-pressure-high-temperature conditions. Therefore, numerous attempts have been made using several approaches (such as regressions and machine learning) to accurately develop models for predicting the Pb. However, some previous models did not study the trend analysis to prove the correct relationships between inputs and outputs to show the proper physical behavior. Thus, this study aims to build a robust and more accurate model to predict the Pb using the adaptive neuro-fuzzy inference system (ANFIS) and trend analysis approaches for the first time. More than 700 global datasets have been used to develop and validate the model to robustly and accurately predict the Pb. The proposed ANFIS model is compared with 21 existing models using statistical error analysis such as correlation coefficient (R), standard deviation (SD), average absolute percentage relative error (AAPRE), average percentage relative error (APRE), and root mean square error (RMSE). The ANFIS model shows the proper relationships between independent and dependent parameters that indicate the correct physical behavior. The ANFIS model outperformed all 21 models with the highest R of 0.994 and the lowest AAPRE, APRE, SD, and RMSE of 6.38%, -0.99%, 0.074 psi, and 9.73 psi, respectively, as the first rank model. The second rank model has the R, AAPRE, APRE, SD, and RMSE of 0.9724, 9%, -1.58%, 0.095 psi, and 13.04 psi, respectively. It is concluded that the proposed ANFIS model is validated to follow the correct physical behavior with higher accuracy than all studied models.

An Accurate Reservoir's Bubble Point Pressure Correlation.

Bubble point pressure (P b) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The P b can be measured from the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the P b. However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the P b applying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters' exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models' robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51%, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the P b.

Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure.

The bubble point pressure (P b) is a crucial pressure-volume-temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model's reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature.

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells.

Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model's reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.