Sensitivity Analysis of Geomechanics Influence on The Success of Hydraulic Fracturing in Shale Gas Reservoir

Shale gas has a permeability of <0.1 mD and a porosity of around 2% - 8% to produce gas that rises to the surface through hydraulic fracturing and horizontal drilling. Geomechanics is one of the important factors that influence the success of a hydraulic fracturing job. Technology in fractures makes geomechanics a clear factor in predicting the success or failure of rocks in deformation and knowing the properties that will be faced by fracture fluids which will later be used to see the effectiveness of fracture fluids in resisting fractures. High operational costs need to be studied further to determine the parameters that affect hydraulic fracturing work, especially from the geomechanical aspect to minimize production failures and work safety. The research conducted this time focuses on the sensitivity of geomechanical parameters by using CMG (GEM) reservoir simulations for reservoir models and conducting Response Surface Methodology (RSM) in selection and ease when applied in the field prior to the hydraulic fracturing process. In this sensitivity study carried out on 5 parameters namely stress, Poisson's ratio, Young's modulus, biot coefficient, and pore pressure. The geomechanical parameter that has the most influence on hydraulic fracturing work based on the sensitivity results carried out through 500 data sets using the Analysis of Variance obtained R2 = 0.99 with the results based on the importance value of the pore pressure variable of 3.8. Then Young's modulus is 0.28, stress is 0.12, Poisson's ratio is 0.08, and biot coefficient is 0.04

An intelligent assistive tool using exergaming and response surface methodology for patients with brain disorders

Intelligent assistive technologies represent a concept that refers to products and services that can offset functional limitations, facilitate independent life, improve their quality of life, and enable people with disabilities to reach their own potential. This paper presents a medical recovery exergaming that includes a Microsoft Kinect Motion Sensor, designed for upper limb rehabilitation, especially for old people with brain disorders. The game is 3D and during the game, the user has to pick up the red or green apples according to a level, and different angles of inclination of the neck, hand, shoulder, and so on are measured and then a total score is generated. To know if the patient has progressed in his medical recovery, the final score should be increased. In order to find the score that a subject without a locomotor system disorder can achieve, we have optimized the game with mathematical modeling and canonical analysis by applying response surface methodology and multiple nonlinear regression. The exergaming based on VR active games represents a useful tool in physical and cognitive rehabilitation for people with motor impairments or brain disorders, considering the advantage of home-training.info:eu-repo/semantics/publishedVersio

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Counterintelligence Technologies: An Exploratory Case Study of Preliminary Credibility Assessment Screening System in the Afghan National Defense and Security Forces

The preliminary credibility assessment screening system (PCASS) is a US-based program, which is currently being implemented by intelligence units of the North Atlantic Treaty Organization (NATO) to make the initial screening of individuals suspected of infiltrating the Afghan National Defense and Security Forces (ANDSF). Sensors have been instrumental in the PCASS, leading to organizational change. The aim of this research is to describe how the ANDSF adapted to the implementation of PCASS, as well as implemented changes since the beginning of the program. To do so, we have conducted a qualitative, exploratory, and descriptive case study that allows one to understand, through the use of a series of data collection sources, a real-life phenomenon of which little is known. The results suggest that the sensors used in PCASS empower security forces with reliable technologies to identify and neutralize internal threats. It then becomes evident that the technological leadership that PCASS provides allows the developing of a relatively stable and consistent organizational change, fulfilling the objectives of the NATO and the ANDSF.info:eu-repo/semantics/publishedVersio

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Improving Personnel Selection through Value Focused Thinking

Personnel selection has always and will continue to be a challenging endeavor for the military special operations. They want to select the best out of a number of qualified applicants. How an organization determines what makes a successful candidate and how to compare candidates against each other are some of the difficulties that top tier organizations like the special operations face. Value focused thinking (VFT) places criteria in a hierarchal structure and quantifies the values with criteria measurements, known as a decision model. The selection process can be similar to a college selecting their students. This research used college student entry data and strategic goals as a proxy for special operations applicants and standards. It compared two case studies of college admissions selection criteria. A sample pool of 8,000 select and 24,000 non-select candidates was generated from real world datasets. VFT was applied to develop a valid admissions selection process model. The schools admissions documentation was used to build the hierarchies, single attribute value functions (SAVF), multi-attribute value functions (MAVF), and weights. A Monte Carlo simulation was used to sample applicants from the generated pool and examined how accurately the models were able to select the correct applicants

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting