With the growing demand of oil worldwide, heavy oil has increasingly become vital in the world energy market. However, further development of heavy oil reservoirs are limited by regular enhanced oil recovery (EOR) methods. In situ upgrading technology provides potential for the development of heavy oil and bitumen reservoirs. This study reviews three categories of in situ upgrading methods: solvent-based, in situ combustion (ISC), and catalytic. Solvent-based methods, including cyclic solvent injection, vapor extraction, and hybrid processes, have recently received attention and have been progressed in both laboratory and field applications. However, high solvent costs in relation to the low price of heavy oil have continued to limit the field applications of these techniques. ISC, which may have the potential to develop particularly harsh reservoirs with extremely viscous crude oil, involves complex reaction mechanisms and consists of three main steps: oxidation, combustion, and gas flooding. Yet, complex operating conditions and a low success rate have restricted its application. Catalytic methods, which have demonstrated the potential to refine and upgrade crude oil in a more economic and environmentally friendly way, are often accompanied by conventional thermal EOR methods, such as steam flooding and ISC, and involve a series of hydroprocessing or hydrotreating reactions, such as hydrocracking, hydrodesulfurization, hydrodenitrogenation, hydrodeoxygenation, and hydrodemetallization. However, the high cost and complexity of the reaction mechanisms have limited their applications.

The Ma5_1-4 submembers of Jingbian gas field in Ordos basin of China belongs to epeiric sea evaporative carbonate tidal flat facies. Supratidal zone of gypsum nodule dolostone and upper part of intertidal zone of gypsum crystal dolostone are most favorable reservoir. The carbonate rocks of Ma5_1-4 had undergone complex diagenesis. Penecontem-poraneous dolomitization and gypsification provided the material foundation of the reservoir. Epidiagenesis selective dissolution of gypseous dolostone constructs the origin shape of pore structure. The dissolution and filling of various minerals in the burial period determine whether the early dissolution pores can be preserved, and the final state of the reservoir. Burial dissolution and filling of various minerals determined the preservation of early dissolved pores and the final condition of the reservoir. The concept of “minus cement porosity” directly reflects that sedimentary facies have great influence on the growth of reservoir in Ma5_1-4. Combined with sedimentary microfacies and diagenetic facies, a concise numerical symbol is used to express semiquantitatively sed-imentary microfacies and diagenesis facies. This descriptive method has great benefit in reservoir evaluation and prediction.

In this paper, the pore structure characteristics of shale samples from the Lower Silurian Longmaxi Formation in South of Sichuan Basin of China were investigated by total organic carbon (TOC) content determination, X-ray diffraction (XRD), scanning electron microscope (SEM), low pressure nitrogen adsorption (LPNA) and high pressure mercury injection (HPMI). The fractal dimension of shale samples was calculated based on Frenkel-Halsey-Hill (FHH) model and thermodynamic relation model. The results showed that the major mineral compositions of shales were quartz and clay content. Organic pores, intergranular pores, intragranular pores, microfractures were widely developed in the shale samples, of which organic pores were the most developed. The pore morphology was mainly ink bottle-shaped pores and slit-shaped pores; the pore size distribution of shale samples was complex with multiple distribution peaks, the pore size between 3 and 40 nm occupied the most of storage space. The fractal dimension D_n1 of pores between 2 nm and 10 nm was 2.7177-2.7933, while fractal dimension D_n2 of pores between 10 nm and 50 nm was 2.2439-2.5468. The fractal dimension Dr of macropores calculated by the thermodynamic model was 2.6401-2.7025.

Most of water based drilling fluids easily penetrate into shale formations and cause serious problems such as well instability, destruction of the formation, wall pipe folding and pipe adhesion. The present research aims to assess the effects of alumina nano-particles (alpha and gamma) on shale/clay stability due to the unique specifications of alumina (alpha and gamma) nano particles. The SEM, XRD, EDAX and AFM analyses of recovered shale samples indicate that the average size of alpha alumina nano-particles coating the shale surface and the pores existing in the shale is between 25 and 35 nm, whereas the average size of gamma alumina nano-particles coating the shale surface and the pores present in the shale is between 20 and 30 nm. In addition, samples recovered by nano-alpha reveal more porosity remained in the shale sample in comparison with the nano-gamma nano-sample. Furthermore, the results obtained from EDAX illustrate a suitable nano-gamma alumina over the recovered shale. The experiment with five types of drilling fluid without the presence of potassium chloride as an inhibitor salt designed. Thus, the water based drilling fluid containing nano-gamma alumina with less than 1 %wt was chosen as the best sample. Generally, the presence of alumina nano-particles optimizes the stability of formations containing ions like shale.

Loss of drilling fluid is a common problem during the drilling of wells and it restricts the appropriate functionality of muds. Drilling fluid loss significantly increases drilling costs and non-productive time as well as the drilling operation risks. Various investigations have been carried out in order to find appropriate mud additives that either block fractures and pores or reduce fluid loss by improving the fluid rheology. Cheap, environmentally friendly and effective additives are still required by the drilling industry. Hence, the application of available materials in each region, to produce appropriate additives, is a challenge for the oil industry.

In this study, Eucalyptus Camaldulensis (EUC) bark powder has been chosen as a new, fibrous, cheap, environmentally friendly and available material to control fluid loss, particularly in southern Iran. Different characterization tests, such as acid dissolution and fluid loss control, were carried out to study the performance of the new proposed additive. Removal by hydrochloric acid and sulfuric acid were studied at various acid concentrations and temperatures. Dynamic fluid loss was also measured at different EUC concentrations. Our study showed that EUC powder can reduce the final fluid loss by 88-97%, the initial fluid loss by 45-66%, and the total loss by 87-94%, which is a satisfactory level.

Stimulated Reservoir Volume (SRV) fracturing is a key technology of unconventional oil and gas exploration and development. To gain a deeper understanding of tight sandstone reservoirs and draw on the development experience of hydraulic fracturing, the authors conduct a large number of detailed investigations of geological characteristics in the regions that have implemented SRV fracturing. Based on the data of rock mechanics parameters, in-situ stress characteristics, brittleness characteristics and natural fractures, the influencing factors of SRV fracturing in tight oil reservoirs were analyzed. The results show that the SRV fracturing is suitable for geological reservoirs with the characteristics of medium to high elastic modulus, low to medium Poisson’s ratio, low stress difference, medium to high brittleness and naturally fractured reservoirs, where natural fractures have a significant impact. Region A, a tight sandstone region, has moderate elastic modulus, low Poisson’s ratio, low ground stress difference and medium brittleness, and has the feasibility of volume fracturing. The field case of the Y325 well shows that SRV fracturing technology has obvious effect on increasing production. This technology is applicable to the Region A.

Formation damage due to fines migration is a major reason for well productivity decline for oil and gas wells. Formation fines are small enough to pass through pore throats causing pore plugging and permeability decline. Different factors affect fines migration such as flow rate, salinity, pH value, reservoir temperature and oil polarity, as well as changes in chemical environment induced by Enhanced Oil Recovery (EOR) agents. This paper focuses on the effect of flow rates on fines detachment from the grain surfaces, which causing permeability reduction. As the fluid inside the reservoir moves towards the wellbore, the fluid velocity increases, when the fluid reaches the critical flow rate these fines can be picked up into the fluid. These fines captured by thinner pore throats causing pore plugging and permeability reduction. Different concentrations of nanoparticles were used to fix these fines on their sources and prevent their mobilization at high flow rates. The unique technique used in this study is changing the potential surfaces between fines and grain surfaces to prevent fines movement above the critical flow rate. SiO₂ and MgO NPs used in this study can be adsorbed on the pore surfaces and reduce the repulsion forces between fines and pore surfaces. SiO₂ and MgO nanoparticles at different concentrations (0.25, 0.50 and 0.75 g/L) were used on treating the Abu-Rawash sandstone reservoir using Formation Damage System Cell FDS-350. The experimental studies showed that using MgO NPs would prevent fines detachment from the pore surfaces and decrease the reduction of permeability at high flow rates more than SiO₂ NPs. The optimum concentration of MgO NPs was at 0.5 g/L as the permeability remediation at this concentration reaches to 64.83%.

This study reports the applicability of quillaja saponin (QS) as a vigorous and environmentally friendly shale swelling inhibitor. QS is a natural surfactant, which is extracted from herbal sources. The inhibition strength of this surfactant was assessed through various experiments, such as sedimentation, inhibition, filtration, particle size, Scanning Electron Microscope (SEM) images, and cutting recovery. Data obtained from these tests illustrated that QS greatly inhibits clays from swelling. The optimal concentration for QS in this intend was 10 g/L. Compatibility of this surfactant with other common additives was also investigated, which showed that it is totally compatible. Finally, the potential inhibition mechanism was assessed through thermal gravimetric analysis (TGA), zeta potential, and contact angle measurement experiments. Surface coating, and wettability alteration of clay particles to the oil-wet state was recognized as the most probable mechanism.

With the enormous increase in the demand for crude oil, and decrease in the resources of conventional oil reservoirs, there is a great need to understand heavy or foamy oil-gas drive mechanism to maximize the oil and gas production. To analyze the real movement of non-viscous heavy oil flow, the characteristic features of the oil-gas mixture has to be estimated to forecast the future potential supply from a heavy oil reservoir. An important question in heavy oil flow under solution gas drive is whether the behaviour of depletion tests can be simulated to model the heavy oil flow behaviour. The main objective of this research is to develop a reliable numerical model for modelling heavy oil flow calibrated with controlled solution gas drive experiments, and that makes a novelty in this manuscript. In this paper, CMG-STARS model which is capable of simulating solution gas drive tests that matched the research experiments. This heavy oil recovery model can determine the relative permeability curves for oil and gas in the dual-phase system using Corey’s relations. At a depletion rate of 0.0418 psi/min, the maximum cumulative oil and gas production was observed to be 13,000 cm³ and 8500 cm³, respectively. The results from the bottom hole pressure and the block pressure simulation runs indicate that the fluid properties such as surface tension plays a significant role in the gas bubble formation. These results are promising, and helps to understand the complex behaviour of heavy oil reservoirs and thus can improve heavy oil recovery.

Compressional and shear wave velocities (V_p and V_s respectively) are essential reservoir parameters that can be used to delineate lithology, calculate porosity, identify reservoir fluids, evaluate fracture and calculate mechanical properties of rocks. In this study, the potential application of intelligent systems in predicting V_p and V_s of reservoir rocks is presented. To date, considerable efforts are being carried out to obtain the best set of parameters capable of predicting V_p and V_s with a high degree of accuracy. Three intelligent models namely artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and least square support vector machine (LSSVM) were used in this study. The different models were based on the available information sourced from wireline log data. Parametric studies showed that measured depth, neutron porosity, gamma-ray, and density log data are vital in predicting both V_p and V_s. In developing the models, a comprehensive dataset available from one of the oil fields in the Norwegian North Basin was used. In evaluating the different models, two different statistical parameters namely Pearson’s correlation coefficient (R²) and root mean square error (RMSE) were considered. It was found that the LSSVM model is the most accurate technique for predicting both V_p and V_s. LSSVM model predicted the V_p with R² and RSME of 0.9706 and 0.0893 respectively. In addition, the model showed an excellent accuracy level in the prediction of V_s with R² and RMSE of 0.9991 and 0.0457 respectively. The proposed approach, if implemented, is crucial for geoscientists, reservoir and drilling engineers working on reservoir characterization and drilling operations.

In this work, numerical models were developed to investigate the critical inclination of a pipeline to eliminate the water accumulation at the floor of the pipe carrying oil-water fluid. Computational fluid dynamics software was used to establish a geometric model of the pipe with various inclination angles, and a grid-independent verification was conducted to determine a reasonable meshing method. Quantitative relationships were determined between the pipe inclination angle and the affecting factors including the flow velocity, viscosity and the pipe diameter, where the water accumulation would not be able to occur. Generally, the critical inclination angle increases with the fluid flow velocity. The refluxing of water is the key mechanism causing the water accumulation at the bottom of the pipe. In addition to the fluid flow velocity, an increase in fluid viscosity and a decrease in the pipe diameter cause an increase of the critical inclination angle that the water phase can be carried away by oil. The model can be used to determine the critical inclination of pipelines carrying oil-water fluid to cause the water accumulation and the operating conditions that can eliminate the accumulation of water phase at the pipe floor.

In order to reduce the non production time of drilling, improve the efficiency and safety of drilling, improve the economic effect of managed pressure drilling (MPD), and realize the intelligent control construction of digital oilfield. Based on the pressure control in MPD, this paper analyzes the pressure control drilling system, takes the wellhead back pressure as the controlled parameter, calculates the mathematical model of the throttle valve according to the characteristics of the throttle valve, the basic parameters and boundary conditions of pressure control drilling, and puts forward an improved particle swarm Optimization PID neural network (IPSO-PIDNN) model. By means of remote communication, VR technology can realize remote control of field control equipment. The real-time control results of IPSO-PIDNN are compared with those of traditional PID neural network (PIDNN) and traditional Particle Swarm Optimization PID neural network (PSO-PIDNN). The results show that IPSO-PIDNN model has good self-learning characteristics, high optimization quality, high control accuracy, no overshoot, fast response and short regulation time. Thus, the advanced automatic control of bottom hole pressure in the process of MPD is realized, which provides technical guarantee for the well control safety of MPD.