MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Low salinity Waterflooding (LSWF) is a proven EOR technique which has emerged in the last few decades. In this, the salinity of the injected water is reduced. Studies have unveiled that the LSWF can improve about 5–38% of oil recovery. This work aims to study the effectiveness of LSWF by modeling Sea waterflooding (SWF) and LSWF using a commercial reservoir simulator and justifying the proposed mechanisms responsible for the incremental oil recovery due to LSWF. A critical parametric study has been undertaken, which led us to conclude that LSWF is a cost-effective, and environment-friendly method that results in additional oil recovery for sandstone reservoirs. © 2023 Taylor & Francis Group, LLC.

World University root node

School of Petroleum Engineering

Engineering and Technology

Petroleum Science and Technology

Investigation of impact of low salinity water flooding on oil recovery for sandstone reservoirs–a simulation approach

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. © The Electrochemical Society

ECS Transactions

A Machine Learning Approach Towards Predicting Formation Permeability Using Real-Time Data

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. © 2021, The Author(s).

Journal of Petroleum Exploration and Production Technology

A review on low salinity waterflooding in carbonates: challenges and future perspective

Low salinity waterflooding (LSWF) is a proven enhanced oil recovery technique which has emerged in the last few decades due to its ability to recover more oil at the lowest cost possible. The technique has gained popularity as enormous laboratory experiments, simulation studies and field executions have shown positive results. In LSWF, the salinity of the injection water is significantly less compared to that of formation water, which is used in conventional waterflooding. The increasing number of matured oil fields globally encouraged the effectiveness of LSWF to increase the overall oil recovery factor and decrease the residual oil saturation. Several authors suggested that an increase of 5–40% in oil recovery can be expected due to LSWF compared to the traditional high salinity waterflooding in sandstone reservoirs. In the presented work, a simulation study has been performed to evaluate the effectiveness of LSWF with a detailed sensitivity analysis to understand the effect of various ions present inside the formation brine or injection brine, reservoir temperature, distance between injector and producer, and various other reservoir parameters on oil recovery factor for sandstone reservoirs. Results confirmed the positive effect of LSWF on oil recovery and proved the advantage of sensitivity analysis to find out the optimum values for the injection scheme to obtain the maximum recovery from a given reservoir. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Modeling Earth Systems and Environment

Assessment of low salinity waterflood parameters from a simulation perspective

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. © Copyright 2018, Society of Petroleum Engineers.

Proceedings - SPE Annual Technical Conference and Exhibition

Application of machine learning for closure pressure determination

The APPEA Journal

Oilfield Data Services Inc Houston Using the wellbore as ad/p meter to calculate gas rate

With the advent and common usage of high resolution tree gauges, downhole permanent pressure/temperature gauges, and continuous flow measurement, many of the common petroleum engineering calculations and analysis tools for production systems can and have been automated. These include: a) Well Test Analysis, b) Well Productivity, c) Static and Flowing Material Balances and Energy Balances. In addition, the use of rigorous wellbore thermal and phase behavior models allows the two gauges (wellhead and downhole) to be used as a giant differential pressure meter - making it possible to calculate gas rates and water cuts independently of the flow measurements, as well as mid-completion bottomhole pressure (BHP). The purpose of this paper is to present the basic physics involved in these calculations/analyses, as well as to discuss the implications that these processes will have on instrumentation selection and on engineering work flows. The crux of the argument for these types of automated systems is that it is much easier for an engineer to check the results than to spend his/her entire day just looking for useful information in the database, then analyzing it (or getting someone else to analyze it). Furthermore, being able to see the "big picture" - seeing what skin, perm, productivity and apparent hydrocarbon reservoir volumes are now and how they have changed with time, allows engineers to make quicker, more accurate decisions. The use of automated analysis also reduces bias - the computer doesn't care what the answer is. This paper will also include several case studies for both oil and gas wells. Copyright 2014, Society of Petroleum Engineers.

Society of Petroleum Engineers - SPE Asia Pacific Oil and Gas Conference and Exhibition, APOGCE 2014 - Changing the Game: Opportunities, Challenges and Solutions