Graphene loaded TiO2 submicron spheres scattering layer for efficient dye-sensitized solar cell.

Dye-Sensitized Solar Cells (DSSCs) have attracted great attention due to environmentally friendly low-cost processing, excellent working ability in diffuse light, and potential to meet the power demands of future buildings due the true class of building integrated photovoltaics (BIPV). Nevertheless, DSSCs have relatively low photoconversion efficiency (PCE) due to multiple issues. Several strategies have been employed to enhance its PCE. For instance, bi-layered structure of photoelectrode i.e., mesoporous TiO2 transparent layer with top scattering layer was introduced which scatter light inside on large angles improves the harvesting ability of photoelectrode thus enhanced PCE. However, scattering layer is composed of aggregated small particles which offer sluggish electron transport due to multiple grain boundaries, consequently, unwanted recombination reaction which leads to poor PCE. This issue has been addressed for transparent layer immensely but ignored for scattering layer. Mostly for scattering layer in previous studies novel structures have been proposed to enhance scattering properties and dye adsorption only. Therefore, in this study for the first time presenting dual functional graphene/TiO2 scattering layer in which solvent exfoliated graphene is incorporated in TiO2 submicron spheres which enhanced electron transport properties, while submicron spheres scatter light effectively. Scattering and electron transport characteristics of DSSCs are thoroughly investigated with the function of graphene loading. Electrochemical impedance spectroscopy (EIS) has revealed that diffusion coefficient length and coefficient and conductivity attained maximum value at 0.01 wt%. while other important parameters such as electron lifetime and electron density in conduction band have been improved till 0.020 wt% graphene loading. However, results indicated that with 0.01 w% graphene 33% higher PCE was achieved than without scattering layer and 13% higher than scattering layer without graphene. The depraving in PCE at >0.01 wt% graphene despite of excellent electron transport improvement is attributed to the loss of diffuse reflectance and higher optical absorption by graphene.

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 Multiwalled Carbon Nanotube-Based Polyurethane Nanocomposite-Coated Sand/Proppant for Improved Mechanical Strength and Flowback Control in Hydraulic Fracturing Applications.

A novel resin-based nanocomposite-coated sand proppant is introduced to address the issue of proppant flowback in post-fracturing fluid flowback treatments and hydrocarbon production. Self-aggregation in the water environment is the most attractive aspect of these developed proppants. In this work, sand was sieve-coated with 0.1% multiwalled carbon nanotubes (MWCNTs) followed by optimized thin and uniform resin (polyurethane) spray coating in the concentration range of 2 to 10%. Quantitative and qualitative evaluations have been carried out to assess the self-aggregation capabilities of the proposed sand proppants where no flowback was witnessed at 4% polyurethane coating containing 0.1% MWCNTs. This applied resin incorporating MWCNT coating was characterized by field emission scanning electron microscopy, and energy-dispersive X-ray spectroscopy depicted the dispersed presence of MWCNTs into polyurethane resin corroborated by the presence of 38% elemental carbon on the sand substrate. Proppant crushing resistance tests were conducted, including proppant pack stress-strain response, compaction, and fines production. It was found that the proposed sand proppant decreased the proppant pack compaction by ∼25% compared to commonly used silica sand with the ability to withstand high closure stress as high as 55 MPa with less than 10 wt % fines production. The surface wettability was determined by the sessile drop method. The application of resin incorporating MWCNT coating layers changed the sand proppant wetting behavior to oil-wet with a contact angle of ∼124°. Thermogravimetric analyses revealed a significant increment in thermal stability, which reached up to 280 °C due to the addition of MWCNTs as reinforcing nanofillers.

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.

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.