The mechanism(s) of Low salinity water flooding (LSWF) has been extensively investigated for 15-20 years, as a cost-effective and environmentally friendly technique for improved oil recovery. However, there is still no consensus on the dominant mechanism(s) behind low salinity effect due to the complexity of interactions in the Crude oil/Brine/Rock (COBR) system. While wettability is most agreed mechanism of low salinity EOR effect. Nevertheless, the mechanism(s) behind the wettability change is debated between multi-component ion exchange (MIE) and double layer expansion (DLE) in sandstone reservoirs. This paper aims to investigate the effectiveness of MIE with a coupled geochemical-reservoir model using published experimental data reported by Nasralla and Nasr-El-Din [1].

We created core-scale numerical models with parameters identical to those used in the experiments. We simulated the low salinity effect using a commercial reservoir simulator, CMG-GEM, by coupling three chemical reactions: (1) aqueous reaction, (2) multi-component ion exchange, and (3) mineral dissolution and precipitation. We modelled the adsorption of divalent cations on the surface of the clay minerals during low salinity water injection. Simulation results were compared with the experimental results.

Simulation results show that the fractional adsorption of divalent cations (Ca²⁺) increased almost 25% by injecting a 2000 ppm NaCl solution, compared to initial 10,000 ppm NaCl. Injecting a 2000 ppm of CaCl₂ solution, however, significantly increased the adsorbed Ca²⁺ from 0.1 to 1, which implies the complete saturation of mineral surface with divalent cations. Moreover, injecting 50,000 ppm of CaCl₂ solution also demonstrated the same effect as the 2000 ppm CaCl₂ solution but with a faster rate.

Upon combining the simulation and experimental results, we concluded that the multi-component ion exchange is not the sole mechanism behind low salinity effect for two reasons. First, almost 10% additional oil recovery was observed from the experiments by injecting the 2000 ppm CaCl₂ compared with 50,000 ppm CaCl₂ solutions. Even though in both cases the surface is expected to be fully saturated with Ca²⁺ according to the geochemical modelling. Second, 6% incremental oil recovery was achieved from the experiments by injecting 2000 ppm NaCl solution compared with that of 50,000 ppm NaCl. Although 25% incremental adsorption of divalent cations (Ca²⁺) were presented during the flooding of the 2000 ppm NaCl solution. Therefore, it is worth noting that the electrical double layer expansion due to the ion exchange needs to be taken into account to pinpoint the mechanism(s) of low-salinity water effect.

Electrofacies are used to determine reservoir rock properties, especially permeability, to simulate fluid flow in porous media. These are determined based on classification of similar logs among different groups of logging data. Data classification is accomplished by different statistical analysis such as principal component analysis, cluster analysis and differential analysis. The aim of this study is to predict 3D FZI (flow zone index) and Electrofacies (EFACT) volumes from a large volume of 3D seismic data. This study is divided into two parts. In the first part of the study, in order to make the EFACT model, nuclear magnetic resonance (NMR) log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations. Then, a graph-based clustering method, known as multi resolution graph-based clustering (MRGC), was employed to classify and obtain the optimum number of Electrofacies. Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network (PNN). In the second part of the study, the FZI 3D model was created by multi attributes technique. Then, this model was improved by three different artificial intelligence systems including PNN, multilayer feed-forward network (MLFN) and radial basis function network (RBFN). Finally, models of FZI and EFACT were compared. Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available. Moreover, they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans. In addition, the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.

This study was originally aimed at suggesting a two-dimensional program for the Steam Assisted Gravity Drainage (SAGD) process based on the correlations proposed by Heidari and Pooladi, using the MATLAB software. In fact, the work presented by Chung and Butler was used as the basis for this study. Since the steam chamber development process and the SAGD production performance are functions of reservoir properties and operational parameters, the new model is capable of analyzing the effects of parameters such as height variation at constant length, length variation at constant height, permeability variation, thermal diffusivity coefficient variation and well location on the production rate and the oil recovery among which, the most important one is the thermal diffusivity coefficient analysis. To investigate the accuracy and authenticity of the model outcomes, they were compared with the results obtained by Chung and Butler. The privilege of this method over that proposed by Heidari and Pooladi lies in its ability to investigate the effect of thermal diffusivity coefficient on recovery and analyzing the effect of temperature distribution changes on thickness diffusivity. Based on the observations, results reveal that the proposed model gives more accurate predictions compared to the old model proposed by Chung & Butler.

Increasing pore pressure due to CO₂ injection can lead to stress and strain changes of the reservoir. One of the safely standards for long term CO₂ storage is whether stress and strain changes caused by CO₂ injection will lead to irreversible mechanical damages of the reservoir and impact the integrity of caprock which could lead to CO₂ leakage through previously sealing structures. Leakage from storage will compromise both the storage capacity and the perceived security of the project, therefore, a successful CO₂ storage project requires large volumes of CO₂ to be injected into storage site in a reliable and secure manner. Yougou hydrocarbon field located in Orods basin was chosen as storage site based on it's stable geological structure and low leakage risks. In this paper, we present a fluid pressure and stress-strain variations analysis for CO₂ geological storage based on a geomechanical-fluid coupling model. Using nonlinear elasticity theory to describe the geomechanical part of the model, while using the Darcy's law to describe the fluid flow. Two parts are coupled together using the poroelasticity theory. The objectives of our work were: 1) evaluation of the geomechanical response of the reservoir to different CO₂ injection scenarios. 2) assessment of the potential leakage risk of the reservoir caused by CO₂ injection.

The importance of the flow patterns through petroleum production wells proved for upstream experts to provide robust production schemes based on the knowledge about flow behavior. To provide accurate flow pattern distribution through production wells, accurate prediction/representation of bottom hole pressure (BHP) for determining pressure drop from bottom to surface play important and vital role. Nevertheless enormous efforts have been made to develop mechanistic approach, most of the mechanistic and conventional models or correlations unable to estimate or represent the BHP with high accuracy and low uncertainty. To defeat the mentioned hurdle and monitor BHP in vertical multiphase flow through petroleum production wells, inventive intelligent based solution like as least square support vector machine (LSSVM) method was utilized. The evolved first-break approach is examined by applying precise real field data illustrated in open previous surveys. thanks to the statistical criteria gained from the outcomes obtained from LSSVM approach, the proposed least support vector machine (LSSVM) model has high integrity and performance. Moreover, very low relative deviation between the model estimations and the relevant actual BHP data is figured out to be less than 6%. The output gained from LSSVM model are closed the BHP while other mechanistic models fails to predict BHP through petroleum production wells. Provided solutions of this study explicated that implies of LSSVM in monitoring bottom-hole pressure can indicate more accurate monitoring of the referred target which can lead to robust design with high level of reliability for oil and gas production operation facilities.

Particle Impact Drilling (PID) is a novel method to improve the rate of penetration (ROP). In order to further improve the performance of PID, an investigation into the effect of single and double particles: (1) diameter; (2) initial velocity; (3) distance; and (4) angle of incidence was undertaken to investigate their effects on broken volume and penetration depth into hard brittle rock. For this purpose, the laboratory experiment of single particle impact rock was employed. Meanwhile, based on the LS-DYNA, a new finite element (FE) simulation of the PID, including single and double particles impact rock, has been presented. The 3-dimensional (3D), aix-symmetric, dynamic-explicit, Lagrangian model has been considered in this simulation. And the Elastic and Holmquist Johnson Cook (HJC) material behaviors have been used for particles and rocks, respectively. The FE simulation results of single particle impacting rock are good agreement with experimental data. Furthermore, in this article the optimal impact parameters, including diameter, initial velocity, distance and the angle of incidence, are obtained in PID.

During fluid injection into a multilayered reservoir, a different pressure gradient is generated across the face of each permeable layer. This pressure gradient generates driving forces in the wellbore during well shut-in that causes the injected fluid moves from higher pressure layers to lower pressure layers, a phenomenon known as interwell cross-flow. Cross-flow behavior depends on the initial pressure in the permeable layers and may be referred to as natural cross-flow (identical or natural initial pressures) and forced cross-flow (different initial pressures because of exploitation). Cross-flow may induce sand production and liquefaction in the higher pressure layers as well as formation damage, filter cake build-up and permeability reduction in the lower pressure layers. Thus, understanding cross-flow during well shut-in is important from a production and reservoir engineering perspective, particularly in unconsolidated or poorly consolidated sandstone reservoirs.

Natural and forced cross-flow is modeled for some injection wells in an oil reservoir located at North Sea. The solution uses a transient implicit finite difference approach for multiple sand layers with different permeabilities separated by impermeable shale layers. Natural and forced cross-flow rates for each reservoir layer during shut-in are calculated and compared with different production logging tool (PLT) measurements. It appears that forced cross-flow is usually more prolonged and subject to a higher flow rate when compared with natural cross-flow, and is thus worthy of more detailed analysis.

Horizontal wells show better affect and higher success rate in low water ratio cement, complex fracture zone, crevice and heavy oil blocks, it is the main measures to expand control area of a single well. Hydraulic fracturing technology is the most financial way to improve the penetration of the reservoir to increase the production. However, compare with the vertical wells, the fracture of Horizontal wells are more complex, and lead to the initiation crack pressure is much higher than vertical wells. In this paper, defined the crack judging basis, and established the finite element model which could compute the initial crack pressure, to research the affection mechanism of perforation azimuth angle, density, diameter and depth, to provide references of perforation project's design and optimize. The research of this paper has significances on further understanding the affection mechanism of perforation parameters.

The significance of gas compressibility factor in petroleum engineering encourages the researchers to employ the most accurate and precise methods for estimation of this factor. Commonly, empirical correlations due to their simplicity have been referred more than other approaches for prediction of Z-factor. There is no clear and reliable report to address an appropriate combination of correlation and mixing rule for each type of gas. In the present study, combination of several empirical correlations and mixing rules is examined and a decision tree is constructed to suggest best combination for each gas system. For this reason, 2329 experimental data were used for analysis. According to the results, Leland-Mueller mixing rule/Sanjari and Lay correlation is the best combination for sour and natural gas. Also, Van Ness-Abbot mixing rule/Hall-Yarborough correlation, Stewart-Burkhardt-Voo mixing rule/Heidarian correlation and Satter-Campbell mixing rule/Papay correlation are the most appropriate combination for gas condensate, binary and ternary mixtures respectively.

For binary mixtures, a robust and novel empirical correlation was developed based on Kay mixing rule to estimate Z-factor. The results employed how good the new correlation is in agreement with the experimental data with significant R-squared 0.9843.

To prevent the deposition of natural gas hydrate in deepwater gas well, the hydrate formation area in wellbore must be predicted. Herein, by comparing four prediction methods of temperature in pipe with field data and comparing five prediction methods of hydrate formation with experiment data, a method based on OLGA & PVTsim for predicting the hydrate formation area in wellbore was proposed. Meanwhile, The hydrate formation under the conditions of steady production, throttling and shut-in was predicted by using this method based on a well data in the South China Sea. The results indicate that the hydrate formation area decreases with the increase of gas production, inhibitor concentrations and the thickness of insulation materials and increases with the increase of thermal conductivity of insulation materials and shutdown time. Throttling effect causes a plunge in temperature and pressure in wellbore, thus leading to an increase of hydrate formation area.

Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry, developing a quick and accurate model, to predict the asphaltene precipitation under different conditions, seems crucial. In this study, a new model, namely genetic algorithm -support vector regression (GA-SVR) is proposed, which is applied to predict the amount of asphaltene precipitation. GA is used to select the best optimal values of SVR parameters and kernel parameter, simultaneously, to increase the generalization performance of the SVR. The GA-SVR model is trained and tested on the experimental data sets reported in literature. The performance of the GA-SVR model is compared with two scaling equation models, using statistical error measures and graphical analyses. The results show that the prediction performance of the proposed model, is highly reliable and satisfactory.

The investment problem of oilfield development is to trade off the investment exploration investment and development investment. With low return on investment got by using the existing method to solve this problem, we construct an optimal model to improve it based on Data Envelopment Analysis (DEA) method and the relations about investment and proven reserves, investment and output as well as production cost. Data Envelopment Analysis (DEA) method is used to present a method to determine the optimal scale of productivity construction investment in unit production. The relation between total cumulated proven reserves and cumulative exploration investment is denoted as an exponential model. The relation among productions and remaining recoverable reserves as well as production cost may be described as an exponential operational cost function. Based on above two relation models and investment effectiveness coefficients of every block, we establish an optimal model whose objective function is net present value (NPV) profit maximum, whose constrain conditions include investment, reserve/production ratio, production and some equality constraints under the mode of sustainable development. It can be solved by genetic algorithms. The result of case study shows that this optimal investment of oilfield development has multi-stage investment structure under given conditions; the model can provide scientific basic theory for oil companies to make a long-term strategic program and investment plan in oil exploration and development, may decrease the subjective blindness in the investment and bring about a reasonable and orderly exploration and development of oil resources.