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Hydrolyzed polyacrylamide (HPAM) is a commonly used polymer for the chemicals, mining and refining processes of hydrocarbon but suffers from a persistent high-temperature instability problem. In contrast, the nanoparticle suspension remains a technical challenge because of the strong interactions of van der Waal forces within nanoparticles, which always encourage aggregation. This research sought to improve nanoparticles (NP) stability and polymer (HPAM) rheological properties to improved hydrocarbon recovery by utilizing synthesized graphene oxide (GO) nanosheets and fumed Aerosil 380 Silica oxide (SiO2). The aqueous nanocomposites based on HPAM-GO and HPAM-SiO2 in aqueous polymeric solutions have been developed, and its viscoelastic and static behaviour is studied. The results imply that by adding fumed silica NP, the viscoelastic behaviour of HPAM is marginally improved, particularly in high temperatures and salinity, however, the inclusion of GO's significantly improves the viscosity and stability of the base polymer fluid at high temperatures. The Fourier data for the transformation of the infrared spectrum confirmed that the hydrogen bonding formed between HPAM carbonyl groups and silica NP surface silanol functionality and covalent interlinking of electrostatic h-bonding between HPAM and functional GO contributed to the improved stabilization and improved rheological performance that helps to recover high salinity and temperature hydrocarbons.

In enhanced oil recovery, different chemical methods utilization improves hydrocarbon recovery due to their fascinating abilities to alter some critical parameters in porous media, such as mobility control, the interaction between fluid to fluid, and fluid to rock surface. For decades the use of surfactant and polymer flooding has been used as tertiary recovery methods. In the current research, the inclusion of nanomaterials in enhanced oil recovery injection fluids solely or in the presence of other chemicals has got colossal interest. The emphasis of this review is on the applicability of nanofluids in the chemical enhanced oil recovery. The responsible mechanisms are an increment in the viscosity of injection fluid, decrement in oil viscosity, reduction in interfacial and surface tension, and alteration of wettability in the rock formation. In this review, important parameters are presented, which may affect the desired behavior of nanoparticles, and the drawbacks of nanofluid and polymer flooding and the need for a combination of nanoparticles with the polymer are discussed. Due to the lack of literature in defining the mechanism of nanofluid in a reservoir, this paper covers majorly all the previous work done on the application of nanoparticles in chemical enhanced oil recovery at home conditions. Finally, the problems associated with the nano-enhanced oil recovery are outlined, and the research gap is identified, which must be addressed to implement polymeric nanofluids in chemical enhanced oil recovery.

The prediction of fluid flow rates is of prime importance for the oil and gas business. Several classical flow rate prediction correlations have been generated and generally used for wells flowing naturally. However, for oil wells flowing artificially, these correlations overlooked many significant fluid and well parameters, leading to erroneous results and inaccurate predictions. This paper used the outputs of a new mathematical model to predict the flow rates of 34 electrical submersible pump (ESP) oil wells from 3 oil fields located in North Africa, possessing a broad range of parameters, to determine a new correlation to predict the oil flow rate. Mathematical procedures for finding the best-fitting curve were used. When compared to the experimentally measured results of another two datasets, the predicted oil flow rates by the new correlation were in good agreement. Indeed, the performance of the new correlation shows reliable prediction results with about ± 10% relative error. Furthermore, the results of this new method were statistically superior to results from previous correlation methods. The correlation that was proposed here exhibits greater accuracy (−5% error) than existing previous correlation methods. The new correlation adapts itself to any ESP oil well and could apply to both onshore and offshore wells.

Spontaneous imbibition (SI) is the principal production mechanism in naturally fractured reservoirs produced by waterflooding and is essential for fluid flow characterization to predict their future performance. As an alternative to the expensive, time-consuming laboratory measurements, 2D images render a different prospect to obtain SI capillary pressure curves, especially for tight reservoirs. This paper introduces a unique approach to infer SI capillary pressure curves from 2D images through integrating image analysis and fractal theory. Using pore-related information obtained from image analysis, we properly represent the pore structure as bundles of tortuous square and triangular tubes with sinusoidally varying radii to imitate cross-sectional variation between pore bodies and throats. Moreover, we simulate the piston-like and snap-off displacement mechanisms to derive an innovative fractal SI capillary pressure model. The developed model considers the contact angle hysteresis caused by surface roughness and heterogeneity of reservoir rocks. The Mayer-Stowe-Princen (MSP) approach is implemented to compute the entry capillary pressure of piston-like displacement. The laboratory-measured porosity and permeability are utilized to determine the model's 3D-related parameters that cannot be inferred from 2D images. The model reliability is verified with the good accuracy of the predicted capillary pressure curves versus laboratory-measured data of five samples from the Liushagang and Huangliu in the South China Sea. Finally, the fundamental parameters influencing the developed SI capillary pressure model are investigated with sensitivity analysis.

This paper presents the use of response surface methodology (RSM) and robust supervised machine learning approaches to model nano-polymeric viscosity. In the absence of studies in the previous research on using these two techniques, this study's key objective was to equate these strategies' efficiency with a nano-polymer solution's viscosity modelling. Synthesized nano-polymer composite is characterized by scanning electron microscope, transmission electron microscope, X-ray diffraction, and thermogravimetric analysis. The research used the Split-Plot Central Composite Design (SP-CCD) of RSM to model five independent parameters for nano-polymeric composite viscosity, namely shear rate, temperature, and concentration nanoparticles, sodium chloride, and calcium chloride. The findings indicate that the polymer solution's viscosity was not equally crucial to all parameters. Besides, a variance analysis (ANOVA) was conducted, and no evidence of inadequacy in the RSM model was provided. The comparative study of the conventional central composite design model and the supervised machine learning model was conducted. The split-plot central composite model gave R2 of 92% and adjusted R2 of 88%. Amongst all supervised machine learning models used, the XGBoost model recorded the highest R2 accuracy of 90% and lowest prediction errors in terms of mean absolute error and root mean square error. XGBoost when compared to other models in terms of akaike information criterion had the highest likelihood of fit making it the best option in analyzing rheological behavior for hybrid polymeric nanofluid.

Higher fluid loss volumes and suspension capability are the critical shortcomings of water-based muds. It can promote problems during drilling operations. The rheological and filtration properties of all drilling muds have been continually considered the essential characteristics for a successful drilling operation. Various irreversible problems are directly associated with drilling fluids rheological and filtration properties such as differential sticking, formation damage, borehole instability and uncontrollable formation fluids intrusion to the wellbore. Drilling fluid is closely associated with the complications or solutions of the mentioned problems. The control of such properties is of paramount importance, especially at high temperature conditions of the wellbore. This study aims to formulate a low-solid drilling fluid for better performance by replacing bentonite in conventional fluids with an equal amount of xanthan gum and tapioca starch. The effects of tapioca starch on rheological and filtration characteristics of water-based muds (WBMs) in static conditions have been investigated at different temperatures and pressures. Various mud blends were prepared containing tapioca starch (TS) and polyanionic cellulose (PAC) in the presence of xanthan gum (XG) at different replacement levels. The impact of tapioca starch on rheological and filtration characteristics were evaluated using rheometer, filterpress (LPLT and HPHT) and scanning electron microscope (SEM). The presence of tapioca starch significantly improved rheological and filtration characteristics of water-based muds (WBMs) and revealed that the low solid blends unveiled an obvious shear thinning behavior. Shear stress (σ) versus shear rate (s−1) data of optimized mud samples was best fitted to the Power Law model with R2 of 0.99. The flow behavior index was decreased from 0.47 to 0.21, while the consistency index was increased from 0.31 to 4.45 with the addition of tapioca starch. Rheological properties were improved with the starch concentration and were found within the American Petroleum Institute (API) recommended range. Filtrate volume for the samples containing tapioca starch in 14 ppb was found 4 ml at a temperature of 212 °F. Filtercake thickness was measured 0.27 mm with the maximum concentration of tapioca starch. SEM images confirmed the tapioca starch showed a slick and compact filtercakes at the temperature of 212 °F. This research helps to encourage the use of biodegradable and renewable additives in drilling fluids for the exploitation and production of hydrocarbons.

The most promising method to improve chemical oil recovery via wettability alteration is associated with colloidal solution such as nanofluid. However, nanofluids show limitations due to nanoparticle agglomeration which may result into pore blockage during its injection in a porous media. The unsuccessful recovery of injected nanoparticles has increased their footprints, which is becoming a serious environmental issue. Therefore, this study reports the use of a co-stabilizer i.e. TiO2 (0.05 and 0.1 wt%) that not only reduces nanoparticle agglomeration in silica nanofluid (SiO2, 0.5 wt%) but also improves its wetting property and oil recovery from synthetic sand-packs of porosity = ∼29–33% and permeability = ∼700–800 mD. In nanofluid synthesis, polymer polyacrylamide was used as base fluid and surfactant [anionic: sodium dodecylsulfate and nonionic: triton x 100] was used to find improvements in wettability alteration, interfacial tension reduction, and oil recovery results of nanofluid. With surfactant, the wetting property of STN (SiO2–TiO2 nanofluid) was found superior (strong water-wet) as confirmed by maximum (67%) reduction in contact angle. Also, interfacial tension of crude oil reduced by ∼91% which increased the chemical oil recovery by ∼78% from sand-pack. Importantly, nanofluid use in porous media is associated with environmental concern. Therefore, each nanofluid was repetitively (3 times) flooded through sand-pack and NP retention was examined using three different characterization methods (contact angle, ultraviolet visible, and field emission scanning electron microscopy) and suitably compared to find least nanoparticle retention in porous media. For surfactant treated nanofluids, nanoparticle retention of 0.03–0.07 wt% was determined while its value was significantly higher (0.31 wt%) for conventional SiO2 nanofluid. Finally, this study suggests that nanoparticle retention of conventional silica nanofluid can be reduced through the selection of a proper co-stabilizer that may help to protect subsurface from pore blockage and other associated environmental issues.

Development of a mathematical solution for injection/falloff problem of an oil-water system for transient data analysis is complicated due to the presence of two mobile phases that create a moving discontinuity. The solutions that have been developed in the past are based on a series of assumptions that make the models limited. The previous models have neglected one or more of the key reservoir and well parameters such as the variation of saturation in the invaded zone, temperature effects, wellbore storage, skin and outer boundary conditions. In this work, a generalized two-step semi-analytical solution method is presented to precisely simulate the pressure transient behaviour for injection and falloff tests following cold water injection for a fully-penetrating vertical well in a warm and bounded oil reservoir in the presence of skin and wellbore storage. The formulated model uses two simplistic steps to solve the complex mathematical problem and improve computational efficiency. The usage of the Laplace-transform finite-difference method to solve the system of equations eliminates the need for temporal discretization, thereby resolving issues related to convergence and time iterations by allowing an unlimited timestep size without the loss of stability and accuracy. The accuracy and consistency of the proposed solution method are assessed by comparing the results with that of ECLIPSE 100 and an excellent agreement is observed. The results suggest that the injection period pressure behaviour is vastly influenced by the injected water propagation under cold conditions, whereas, the falloff period pressure behaviour provides information of the water bank under cold conditions and the uninvaded zone under hot conditions. The comparison of the non-isothermal and isothermal pressure behaviour shows that the isothermal model can be improved using modified fluid viscosities to match the non-isothermal pressure behaviour that not only could minimize the mathematical complications, but also the computation time.

This work presents a comprehensive review on the use of bio-inhibitors for stabilizing gas/oil shales during drilling and hydraulic fracturing. The swelling and dispersion inhibiting potentials and mechanisms of 23 bio-inhibitors were analytically reviewed and compared with existing conventional inhibitors. Relevant data on the influence of bio-inhibitors to shale swelling, YP, contact angle, and zeta potential is provided. Also, the toxicity, biodegradability and production viability of bio-inhibitors are discussed. The review indicated that bio-inhibitors are potential candidates for clay swelling inhibition considering their low-cost, performance and environmental friendliness. The work provides guidelines and recommendations in selecting efficient shale destabilization inhibitors.

The significant challenge in the oil and gas industry is the simultaneous measurement of commingled gas, oil and water streams, using the three phase test separator or multiphase flow meter (MPFM). A major issue in the application of using either test separator or MPFM in oil fields is the uncertainty of the measurements, due to different process and operations conditions. This paper proposes a new computational method to estimate the electrical submersible pump (ESP) oil wells flow rates. The research idea is to close the wellhead wing valve, to measure the wellhead flowing pressure before and after closing the well head valve, and to record the total shut-in time period. Explicit physics concepts for estimations of the multiphase fluid flow rate in a vertical pipe were employed. The formulas deal with changes of fluid flow parameters along the vertical pipe in the well, as a function of pressure and temperature variations with depth. A Microsoft visual basic program was also developed based on the oil mechanistic and empirical equations that can estimate oil rate for ESP oil wells. The new method was applied on 34 ESP oil wells in a North African oil field and lead to very reliable estimation results, which have about a +/−10% relative error, and can be applied to all fluid types and under any production conditions.