Low salinity waterflooding has gained significant attention and importance in the last decade, as it is seen as an impactful method for recovery of additional oil from carbonate reservoirs. Existing literature does not do justice to the underlying mechanisms that aid in the recovery of additional oil from such rock types. In this paper, we present a comprehensive review of the research conducted on low salinity waterflooding in carbonates and further provide a detailed and critical analysis on the same. The intention of this paper is also to present a condensed research summary on the said topic, and to chart out a detailed roadmap for future work, thereby opening the possibilities of new avenues of research in the field.

Hydraulic fracturing is a method in which fluid is pumped at elevated pressures to break down the formation, and create a conductive pathway for production of hydrocarbon fluids. Understanding the stress environment of the rock is critical for a better design and successful execution of a fracturing treatment. More often than not, a formation breakdown pressure is equal to or in close approximation of minimum horizontal or in-situ stress, also termed as closure pressure. Various analytical methods such as G-Function plot, G-dP/dG plot, square-root of time plot etc. are used for the determination of closure pressure, and have been implemented since the inception of hydraulic fracturing as a way to better design fracture treatments. These methods are prone to have subjectivity due to the experience and knowledge of the person analyzing the data, which calls for a need to more objectively analyze such data, in order to better predict the closure pressure. Machine learning is a method to teach computers to implement a predesigned algorithm and execute tasks without having to explicitly program them. It helps create significantly complex mathematical models which automate processes based on critical learning parameters, and predict within a certain acceptable degree of accuracy. In this paper, Artificial Neural Networks (ANN), a machine learning methodology, has been applied in order to minimize the subjectivity in predicting the value of closure pressure. Artificial neural networks, similar to neural networks of the brain, are a system comprising of various neurons. These neurons are organized in layers namely input layer (consisting of input neurons), output layer (consisting of output neurons or results) and multiple hidden layers. The number of input, hidden and output neurons depend on the parameters affecting the end-result. An ANN has been designed, taking into consideration critical parameters on which closure pressure depends. The model identifies and learns from the patterns in the data and predicts the required output. This output is then compared with the actual results in order minimize the error. The objective is to minimize the error so as to get a close match for the given data. In this paper we have kept the ratio of learning to testing at 80:20, which means that of all the available data, 80% is used for training the model and the rest 20% is used for testing the model. Results from this work point to the fact that the ANN was able to predict the closure pressure with reasonable accuracy.

The common use of high-resolution tree gauges and downhole permanent pressure/temperature gauges has made it possible to use the measured pressure drop in the wellbore to directly and accurately calculate the gas rate. This is accomplished by first combining an equation of state with a dynamic heat transfer model to create a phase-thermal model (PTM). The PTM is then integrated with a direct solution to the mechanical energy balance (MEB) for flow in pipes. The results obtained using this technique can be as accurate as, or in some cases more accurate than, conventional rate measurements. Since the wellbore may also be used for fluid density validation, the effective gas gravity (an input for many conventional flow rate calculations) may also be determined during shut-ins and used as an input to improve the accuracy of meter provers. The purpose of this paper is to explain the physics behind the gas rate calculation and to present case study results from the implementation of this method in both real-time and historic data processing. The paper will also discuss the limitations of this method and the range of potential applications.

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Reservoirs are hydrocarbons (oil and gas) bearing subsurface structures (formation) in which wells are drilled to produce the fluids to the surface. Well testing is a method of studying pressures and their corresponding rates from an individual well, to analyze the various characteristics of a reservoir, which helps in optimum management of the production operations. In this paper, we have applied one of the most popular supervised learning algorithms known as Artificial Neural Networks (ANN) for predicting the permeability (conductivity) of the formation. The well testing data consisting of well head pressure, down hole gauge pressure, flow rates for oil and water, P*, P-1hour, etc. were used as input features for training the model. A 4-layer dense ANN architecture consisting of one input and output layer each and two hidden layers was built for training and testing the model.