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.

Drilling fluid constitutes an important part of the drilling operations. Drilling mud is circulated into the wellbore to minimize the formation damage, transport cuttings from the bottom to the top of the well, cool and lubricate the bit, and maintain the stability of shale formations. Gel strength property of drilling fluids plays a key role in drilling multilateral and long horizontal reservoir sections. Losing the gel strength will accumulate drilled cuttings and as a result, sticking of the drill string. Solving this issue takes a long time and increase the total cost of the drilling operation. It was observed that, by increasing the temperature to 200°F, the gel strength of calcium carbonate water-based drilling fluid reached zero lb/100 ft2. The objectives of this paper are to: (1) determine the rheological properties of calcium carbonate water- based drilling fluid over a wide range of temperature, (2) assess the effect of using clays (bentonite) to solve the gel strength problem associated with the current field formulation of calcium carbonate water- based drilling fluids, and (3) optimize the weight percent of bentonite. Rheological properties such as the apparent viscosity, plastic viscosity, yield point and gel strength were measured by using a high-pressure high-temperature rheometer. At low temperatures (85°F), the calcium carbonate water-based drilling fluid showed no change in the gel strength with time. However, at high temperatures, the gel strength starts decreasing with time. Rheological properties confirmed that the gel strength of the calcium carbonate water-based drilling fluid reached zero lb/100 ft2 by increasing the temperature to 200°F. This issue was solved by adding different concentrations of bentonite. The bentonite concentration was varied from 3 wt.% to 10 wt.%. At low bentonite concentrations (3.33 wt.%), the gel strength still reduced with time. At high bentonite concentration (10 wt.%), the gel strength increased with time. The optimum concentration of bentonite was 6.66 wt.%, which yielded a flat rheology profile of the gel strength. These results confirmed that the rheological properties of the water-based drilling fluid were optimized by using clays (bentonite). The novelty of this research is the complete investigation of the drilling fluid properties especially the gel strength with time over a wide range of temperature. The results obtained can leads to the development of a cheap solution without any side effect.

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.

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It is very important to determine or predict the bubble point pressure (BPP) with high accuracy in petroleum industry. Laboratory measurement of the BPP requires collecting actual samples from the bottom of the wellbore and simulates the reservoir conditions at the lab. This operation takes long time and high cost. To overcome this issue, many empirical correlations were developed to predict the BPP with wide range of average percent error. In this research, we will use artificial intelligent (AI) techniques to predict the bubble point pressure using published data (760 data sets). Two different AI techniques will be used, artificial neural network (ANN) (back propagation network (BPN) and radial basis functions networks (RBF)), and fuzzy logic tool (FL) to develop the model. The obtained results will be compared with the available correlations in the literature. The results obtained showed that all AI models were able to predict the bubble point pressure with a high accuracy. The new fuzzy logic (FL) model outperforms all the artificial neural network models and the most common published empirical correlations. BPN, RBF and FL models provide predictions of bubble point pressure with correlation coefficient of 0.9926, 0.9969, and 0.9995, respectively.