Current global energy demand and supply gap needs the best engineering methods to recover hydrocarbons from the unconventional hydrocarbon resources. Unconventional resources mostly found in highly stressed, over pressured, and deep formations, where the rock strength and integrity both are very high. The pressure at which the rock fractures or simply the breakdown pressure is directly correlated with the rock tensile strength and the stresses acting on them from the surrounding formations. When fracturing these kinds of rocks, the hydraulic fracturing operation becomes much more challenging and difficult, and in some scenarios reached to the maximum pumping capacity limits. This reduces the operational gap to optimally placed hydraulic fractures. In the present research study, a novel thermochemical fracturing approach is presented to reduce the breakdown pressure of the high-strength layered formations. The new approach not only reduces the breakdown pressure of the layered rocks but also generate highly conductive fractures which can penetrate in most of the layers being subjected to fracturing. The hydraulic fracturing experiments presented in this study are carried out on four layered cement block samples. The composition of cement blocks is synthesized in this way that it simulates the real rocks. The results showed that the newly proposed thermochemical fracturing approach reduced the breakdown pressure in layered rocks from 1495 psia (reference breakdown pressure recorded from conventional hydraulic fracturing technique) to 1107 psia. The post treatment experimental analysis showed that the thermochemical fracturing approach resulted in deep and long fractures, passing through majority of the layers while conventional hydraulic fracturing resulted in a thin fracture affected only the top layer. Thermochemical fluids injection caused the creation of microfractures, improved the porosity and permeability, and reduces the Young's modulus of the rocks. The new technique is cost effective, non-toxic, and sustainable in terms of no environmental hazards.

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A major challenge in carbonate reservoirs is the highly-fractured nature of the rock. The flow rate may be high or low depending on the targeted fracture clusters. In addition, it is possible that flow rates vary from one region of the reservoir to another. Smart wells furnished with smart completion strategy presents great prospects to produce such reservoirs intelligently, thereby, helping to deal with heterogeneities rather smartly. It is established that early water break-through occurs when multi-lateral wells are completed with constant choke settings, and therefore one way to mitigate this problem is using smart completions that manage the unexpected production through fractures, thereby increasing ultimate recovery. The early water breakthrough is obvious because if a lateral section intersects a clusters of fracture zone, there is a possibility that these fractures may connect with the water zone that may trigger the breakthrough. This can be managed by preferentially regulating production from manifold laterals. The evident communication among the various laterals of the mother bore raises difficulty in optimizing the production from the variable productivity intervals. In theory, the optimization scheme of smart completion involves different constraints, nevertheless, the settings of the smart inflow control valve (ICV) is the single most important parameter that may prove to be the differentiating factor between a high producing well to a poorly producing one. This study engrosses its effort on the reservoir engineering characteristics of finding the optimum choke setting that would lead to maximum recovery. Computational Intelligence through Particle Swarm Optimization (PSO) is utilized as the integral algorithm to determine the optimal ICV configuration for a fishbone well in a naturally fractured carbonate reservoir. A commercial black oil simulator was used to determine the objective function; whose role here is to evaluate the fitness of a configuration of the choke; this was carried out under a workflow programmed in the MATLAB programming language that coupled the optimization algorithm with the numerical simulator. A single fishbone well, having 15 laterals was studied in order tot see the effect of the fracture network on the water breakthrough and consequent impact on recovery. Three different scenarios are developed to see the impact of optimization; a base case employing only multilateral well technology without the smart well completion, a smart well completion scheme with no optimization and finally the optimized smart well completion. The results very sequentially clarify the need for not only optimization but also highlights the role of intelligent completions for wells in the reservoir being studied. It is evident that without using smart wells, the water breakthrough is relatively earlier and produces less hydrocarbons, but as the use of smart wells is incorporated, the results start improving and for complete optimization scheme of the ICVs, it is observed that the recovery has increased by almost 80% from 21% to 38%. Moreover, the time to water breakthrough and eventually the cumulative water cut has also been managed quiet significantly.

Smart water (or low salinity water) flooding has been an emerging technology in the petroleum industry since last two decades. Low capital cost and operating expenses of this flooding make it attractive for the petroleum industry. This paper examines the economic feasibility of the injection of smart water and compares with other conventional water flooding techniques. Optimization has also been done with different dilution schemes through particle swarm optimization. This study analyzes the effect of smart water and sequential dilution of injected sea water through reservoir modeling. A three-dimensional black oil reservoir model is developed by using ECLIPSE 100. In addition, this study presents the economic feasibility of the injection of smart water and compares with other conventional water flooding techniques. The study is divided into four cases: i) oil is produced without water flooding, ii) formation water is injected in the reservoir, iii) sea water is injected in the reservoir, and iv) water injection is taken place by sequential dilution of high salinity water. In each case, economic evaluation is completed by calculating the costs and revenues generated by water injection, and oil prod uction. The results show that sea water injection did not give additional oil recovery compared to formation water injection for our case. However, additional results show that sequential dilution flood recovers more oil than sea water and formation water injection. Moreover, five main parameters are optimized such as number of cycles of different salinities, duration of various cycles, salinity values for different cycles, injection rate and production rate. Optimization results show even better results than sequential dilution. The optimization also shows that the additional oil recovery is achieved when the dilution sequence is altered. This outcome illustrates that increased oil recovery is not only dependent on step wise reduction of sea water salinity but also with the variation of dilution pattern. This paper presents a novel technique for the reservoir engineers to study smart water flooding with different perspective. Sequential dilution has been an acceptable technique for increasing oil recovery. However, change of the dilution pattern could be a good alternative and thus provides a cost-effective technique as compared to sequential dilution.

Research into the use of polymers for enhanced oil recovery (EOR) processes has been going on for more than 6 decades and is now classified as a techno-commercially viable option. A comprehensive evaluation of the polymer's rheology is pivotal to the success of any polymer EOR process. Laboratory-based evaluation is critical to EOR success; however, it is also a time/capital consuming process. Consequently, any tool which can aid in optimizing lab tests design can bring in great value. Accordingly, in this study a novel predictive correlation for viscosity estimation of commonly used "FP 3330S" EOR polymer is presented through use of cutting-edge machine learning neural networks. Mathematical equation for polymer viscosity is developed using machine learning algorithms as a function of polymer concentration, NaCl concentration, and Ca2+ concentration. The measured input data was collected from the literature and sub-divided into training and test sets. A wide-ranging optimization was performed to select the best parameters for the neural network which includes the number of neurons, neuron layers, activation functions between multiple layers, weights, and bias. Furthermore, the Levenberg-Marquardt back-propagation algorithm was utilized to train the model. Finally, measured and estimated viscosities were compared based on error-analysis. Novel correlation is developed for the polymer that can be used in predictive mode. This established correlation can predict polymer viscosity when applied to the test dataset and outperforms other published models with average error in the range of 3-5% and coefficient of determination in excess of 0.95. Moreover, it is shown that neural networks are faster and relatively better than other machine learning algorithms explored in this study. The proposed correlation can map non-linear relationships between polymer viscosity and other rheological parameters such as molecular weight, polymer concentration, and cation concentration of polymer solution. Lastly, through machine learning validation approach, it was possible to examine feasibility of the proposed models which is not done by traditional empirical equations.