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.

Stability, Rheological Behavior, and pH Responsiveness of CTAB/HCl Acidic Emulsion: Experimental Investigation.

Cetyltrimethylammonium bromide (CTAB) surfactant was proven to be a reliable emulsifier for creating stable emulsions used for drilling, well stimulation, and EOR. The presence of acids like HCl during such operations may lead to the formation of acidic emulsions. No previous comprehensive investigations have been done to study the performance of CTAB-based acidic emulsions. This paper, therefore, presents experimental investigations of the stability, rheological behavior, and pH responsiveness of a CTAB/HCl-based acidic emulsion. The effects of temperature, pH, and CTAB concentration on the emulsion stability and rheology have been investigated using a bottle test and a TA Instrument DHR1 rheometer. Viscosity and flow sweep were analyzed for the steady state at a shear range of 2.5-250 s-1. For the dynamic tests, the storage modulus (G') and loss modulus (G″) were observed by applying the oscillation test at the range of shear frequency from 0.1 to 100 rad/s. The results revealed that the emulsion exhibits steady rheological behaviors ranging from Newtonian to shear-dependent (psedosteady), depending on the temperature and CTAB concentration. The tendency of the emulsion to exhibit a solid-like behavior is also dependent on CTAB concentration, temperature, and pH. However, the pH responsiveness of the emulsion is more significantly observed within the acidic range of the pH.

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.

Determination of the Gas-Oil Ratio below the Bubble Point Pressure Using the Adaptive Neuro-Fuzzy Inference System (ANFIS).

Determining the solution gas-oil ratio (R s) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas-oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the R s below the bubble point pressure. A total of 376 pressure-volume-temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method's performance in predicting the R s at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations.

Do Leadership, Organizational Communication, and Work Environment Impact Employees' Psychosocial Hazards in the Oil and Gas Industry?

Workplace hazards can have a significant influence on a worker's physical and mental health, reducing an organization's effectiveness in terms of safety. However, psychosocial hazards are being recognized as a crucial component that must be addressed for the individual's and organization's safety. The purpose of this research was to propose and statistically evaluate a brief theoretical framework based on leadership, organizational communication, work environment, and psychosocial hazards in Malaysia's upstream oil and gas sector. The framework was tested on 380 Malaysian upstream oil and gas workers. The collected data were analyzed using partial least squares and structural equation modelling (PLS-SEM). The study's findings revealed that in the Malaysian oil and gas industry, leadership, communication, and work environment negatively influenced the psychosocial hazards. This negative association between predictors and psychosocial hazards, particularly job expectations, control, role, and relationships, indicates new grounds for research. It is discussed how the findings could be used to track employees' well-being over time and generate focused treatments.

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.

Impact of Safety Culture on Safety Performance; Mediating Role of Psychosocial Hazard: An Integrated Modelling Approach.

We conceptualize that safety culture (SC) has a positive impact on employee's safety performance by reducing their psychosocial hazards. A higher level of safety culture environment reduces psychosocial hazards by improving employee's performance toward safety concerns. The purpose of this study was to evaluate how psychosocial hazard mediates the relationship between safety culture and safety performance. Data were collected from 380 production employees in three states of Malaysia from the upstream oil and gas sector. Structural equation modeling was implemented to test the suggested hypotheses. The proposed model was evaluated using structural equation modeling. A stratified sampling with a Likert 5-point scale was used to distribute the questionnaires. Furthermore, the proposed model was tested using the simulation of the structural equation and partial. According to our findings, all hypotheses were significant. A review of prior studies was used to select the items of the dimension for the data collection. Safety culture was assessed with psychosocial hazard to determine its direct and indirect impact on safety performance. Results suggest that to enhance safety performance (leading and lagging), psychosocial concerns in the workplace environments should be taken into consideration by employees. In addition, the findings showed that the psychosocial hazard fully mediates the relationship between safety culture and safety performance.

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.

Experimental Investigation and Performance Evaluation of Modified Viscoelastic Surfactant (VES) as a New Thickening Fracturing Fluid.

In hydraulic fracturing, fracturing fluids are used to create fractures in a hydrocarbon reservoir throughout transported proppant into the fractures. The application of many fields proves that conventional fracturing fluid has the disadvantages of residue(s), which causes serious clogging of the reservoir's formations and, thus, leads to reduce the permeability in these hydrocarbon reservoirs. The development of clean (and cost-effective) fracturing fluid is a main driver of the hydraulic fracturing process. Presently, viscoelastic surfactant (VES)-fluid is one of the most widely used fracturing fluids in the hydraulic fracturing development of unconventional reservoirs, due to its non-residue(s) characteristics. However, conventional single-chain VES-fluid has a low temperature and shear resistance. In this study, two modified VES-fluid are developed as new thickening fracturing fluids, which consist of more single-chain coupled by hydrotropes (i.e., ionic organic salts) through non-covalent interaction. This new development is achieved by the formulation of mixing long chain cationic surfactant cetyltrimethylammonium bromide (CTAB) with organic acids, which are citric acid (CA) and maleic acid (MA) at a molar ratio of (3:1) and (2:1), respectively. As an innovative approach CTAB and CA are combined to obtain a solution (i.e., CTAB-based VES-fluid) with optimal properties for fracturing and this behaviour of the CTAB-based VES-fluid is experimentally corroborated. A rheometer was used to evaluate the visco-elasticity and shear rate & temperature resistance, while sand-carrying suspension capability was investigated by measuring the settling velocity of the transported proppant in the fluid. Moreover, the gel breaking capability was investigated by determining the viscosity of broken VES-fluid after mixing with ethanol, and the degree of core damage (i.e., permeability performance) caused by VES-fluid was evaluated while using core-flooding test. The experimental results show that, at pH-value ( 6.17 ), 30 (mM) VES-fluid (i.e., CTAB-CA) possesses the highest visco-elasticity as the apparent viscosity at zero shear-rate reached nearly to 10 6 (mPa·s). Moreover, the apparent viscosity of the 30 (mM) CTAB-CA VES-fluid remains 60 (mPa·s) at (90 ∘ C) and 170 (s - 1 ) after shearing for 2-h, indicating that CTAB-CA fluid has excellent temperature and shear resistance. Furthermore, excellent sand suspension and gel breaking ability of 30 (mM) CTAB-CA VES-fluid at 90 ( ∘ C) was shown; as the sand suspension velocity is 1.67 (mm/s) and complete gel breaking was achieved within 2 h after mixing with the ethanol at the ratio of 10:1. The core flooding experiments indicate that the core damage rate caused by the CTAB-CA VES-fluid is ( 7.99 % ), which indicate that it does not cause much damage. Based on the experimental results, it is expected that CTAB-CA VES-fluid under high-temperature will make the proposed new VES-fluid an attractive thickening fracturing fluid.

Chemical Sand Consolidation: From Polymers to Nanoparticles.

The chemical sand consolidation methods involve pumping of chemical materials, like furan resin and silicate non-polymer materials into unconsolidated sandstone formations, in order to minimize sand production with the fluids produced from the hydrocarbon reservoirs. The injected chemical material, predominantly polymer, bonds sand grains together, lead to higher compressive strength of the rock. Hence, less amounts of sand particles are entrained in the produced fluids. However, the effect of this bonding may impose a negative impact on the formation productivity due to the reduction in rock permeability. Therefore, it is always essential to select a chemical material that can provide the highest possible compressive strength with minimum permeability reduction. This review article discusses the chemical materials used for sand consolidation and presents an in-depth evaluation between these materials to serve as a screening tool that can assist in the selection of chemical sand consolidation material, which in turn, helps optimize the sand control performance. The review paper also highlights the progressive improvement in chemical sand consolidation methods, from using different types of polymers to nanoparticles utilization, as well as track the impact of the improvement in sand consolidation efficiency and production performance. Based on this review, the nanoparticle-related martials are highly recommended to be applied as sand consolidation agents, due to their ability to generate acceptable rock strength with insignificant reduction in rock permeability.