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Additive Manufacturing (AM) or 3D printing is a relatively new manufacturing technology and Powder Bed Fusion (PBF) is one of the major modalities of this technology. There is a misconception that it is "plug-and-play" with no greater risks involved. The focus of this study is to raise awareness about the hazards associated with, AM operations and waste management, mitigation and change the mindset. In the AM processes a lot of chemicals are used in the whole AM cycle; feedstock, pre-processing and post-processing. Feedstock for metallic 3D printers is very fine metal powder (less than 100µm in size) with various health and fire hazards associated with it. This metal powder is spread on the build plate to create layers for selectively melting it until the whole structure is built. Un-melted powder is recycled for the next manufacturing job. However, some of the powder will remain on the manufactured part structure and some will go to multiple filters during the process. This powder is to be safely removed from the built part and managed as hazardous waste. Similarly, clogged filters are also to be safely removed and treated as hazardous waste. A well-defined chemicals waste management process for 3D printing is presented and discussed in this paper. Examples of waste sources are metal powder characterization, unloading printed jobs and contaminated tools and equipment. In this paper, the authors have presented topics related to AM waste management, including but not limited to the sources of chemical wastes, waste types that generated by AM processes, waste control and management, waste disposal, international standards associated with hazardous waste management and some recommendations and remedies.

Maintaining a stable borehole is one of the major tasks during drilling operations. During the drilling, borehole breakout and drilling induced fractures are the two main instability problems which may lead to stuck pipe, sidetracking, and loss of circulation. To evaluate the stability of a wellbore, a constitutive model is required to estimate the stresses around the wellbore coupled with a failure criterion to predict the ultimate strength of reservoir rocks. The Mohr-Coulomb failure criterion is one of the commonly accepted criteria for rock strength estimation at a given state of stress. This failure criterion is mainly contributed from the cohesion and coefficient of internal friction parameters, which are determined by laboratory measurements. The laboratory measurements, although more reliable, are expensive and time-consuming. This paper discusses artificial intelligence models particularly multilayer perceptron (MLP) and support-vector regression (SVR) for predicting cohesion and coefficient of internal friction from elemental spectroscopy and petrophysical properties. Elemental spectroscopy, density, porosity, cohesion, and coefficient of internal friction data presented in this paper are based on various geological formations. Cohesion and coefficient of internal friction are determined through a rock mechanical test in the laboratory, while elemental spectroscopy data were obtained from X-ray fluorescence (XRF) analysis. We divide the data set into training and testing data. Training data is used to train MLP and SVR then establishes the cohesion prediction models. Similarly, training data is used to train and construct the MLP and SVR-based coefficient of internal friction models. Both models are then examined using the testing data. Cohesion and coefficient of internal friction predicted from MLP and SVR match well with the laboratory measurements. Two quantitative measures for estimation accuracy are used including coefficient of determination and mean absolute percentage error. Cross-correlation plots of predicted cohesion and coefficient of internal friction and the experimental results show very good coefficient of determination and relatively small error. The results demonstrate that amongst the MLP and SVR models, the models whose inputs are grain density, porosity, and elemental spectroscopy are the best models. From a practical point of view, the application of artificial intelligence techniques as a new method for indirect estimation of rock failure parameters are beneficial especially when the amount of core samples are relatively few.

The recent crash in the oil market has allowed the industry to reduce the pace of evaluation and completion decisions in unconventional reservoirs, and turn to a more science-based decision-making process for project execution. The traditional stimulation design based on the geometric spacing of induced fractures is now gradually changing to geological spacing (i.e., a design based on an understanding of the reservoir geology) to enhance hydraulic fracture stimulation effectiveness for drastically reduced cost. A methodical rock texture characterization of core samples and cuttings can provide powerful information that can be used reliably and cost-effectively to optimize fracture stimulation designs by placing frac stages based on rock characteristics. This paper presents a new method to quantify rock texture based on automated petrographic analysis that uses advanced microscopy image analysis from scanning electron microscopy (SEM) and optical microscopy. A procedure called "quantitative evaluation of minerals using a scanning electron microscope" (QEMSCAN) and optical microscopy analyses were used to image rock samples prepared from cores and cuttings. Rock texture parameters were extracted automatically using new digital data processing techniques. The information from automated petrographic analysis was used to determine the spatial distribution of all components including mineral composition, framework grains, matrix, cement, grain size and shape, pore size and shape, modes of contact between grains and the nature of porosity. The results showed that while mineral composition of rock is important, texture characterization is far more significant to understand rock behavior than has been reported in the industry. Our results demonstrate the importance of quantitative microscopy and how it can provide an understanding of the key relationship between rock texture and rock behavior. A new method was produced to characterize rock texture quantitatively from advanced image analysis with the aid of an automated petrographic system.

Today’s smart phones are comprised of a variety of sensors and hardware, based on which a number of specialized applications have been developed. Unoptimized use of these hardware makes the power consumption of smart phones, a matter of serious concern. Most of the applications installed in smart phones keep track of user location and exchange user data with the servers. Even though a majority of the applications do not need real-time data exchange, they store the data until the cellular data network becomes available and then transfer the data to the servers. This consumes a lot of power from the handset, as cellular data transfer consumes more power than the traditional Wi-Fi networks. For location tracking, most of the applications use GPS, although these applications may not need precise location information. This results in loss of smart phone battery power. In our work, we have modified a health activity tracker app that consumes a lot of power. We tried to optimize power consumption by adding these features: We have replaced simple GPS location tracking by using a combination of network provider information and accelerometer for tracking the activity of the user; Network communication is done, without draining the power of the phone, by transmitting data when it is connected to a Wi-Fi network, but reducing the frequency of transmission when it is connected to a cellular network. The system also identifies the availability of free Wi-Fi by tracking the user’s location while connected to the cellular network. Thus the power consumption by the smart phone is reduced.

Accurate determination of organic-rich shale permeability is still a major challenge. Various methods have been proposed to measure the permeability on core plugs or crushed samples under various stress conditions using different fluids. Permeability obtained from core plugs and their crushed samples could differ by two orders of magnitude, potentially painting very different views of the reservoirs and resulting in differences in asset development workflows. This situation only reinforces the need for considerable additional focused work to quantify tight rock permeability and better understand the measurement method dependence. This paper presents the experimental comparison of three different unsteady-state transient methods for measuring the permeability of organic-rich shale plugs: pressure build-up, pulse-decay and oscillating pulse techniques. Permeability measurements are conducted isothermally using nitrogen gas on core plugs from the Barnett, Eagle Ford, Marcellus and Mancos formations at the same confining pressure and pore pressure. These plugs differ in mineralogy, total organic carbon (TOC), nuclear magnetic resonance (NMR) and helium porosity. Fractures are observed through the horizontal samples along the gas flow direction, while vertical samples do not have fractures. For each plug, the permeability tests begin with the pressure build-up measurement, and are followed sequentially by the pulse-decay and oscillating pulse methods whenever applicable. The plugs are then cut into smaller sizes for continuation of permeability tests and investigation of the permeability dependence on the plug size. For the samples analyzed, the permeability measured from the pulse-decay method is essentially identical to that from the oscillating pulse method. Compared to these two methods, the pressure build­up test generally gives a relatively higher value when requiring an independent porosity measurement to compute the permeability, while it gives a relatively lower value when no porosity is needed. However, the permeability difference among these methods is generally small. This indicates that the pressure build-up test can be used to perform permeability measurement on large core plugs that cannot be tested using the pulse-decay or oscillating pulse methods. There is no trend observed on the permeability dependence on the plug sizes used. Depending on fracture distribution and connectivity, the measured permeability of horizontal samples is randomly affected by the sample sizes, indicating multiple samples with various sizes may be required to obtain a representative permeability value for horizontal plugs. Vertical plugs show little permeability dependence on the sample sizes. Consequently, a small vertical plug can be used for very tight rocks enabling quicker matrix permeability measurements.

Selecting a drilling fluid based on learnings from conventional reservoirs can be a wrong choice for unconventional drilling. Improper selection of drilling fluid may cause strong shale-fluid interaction and, consequently, wellbore instability issues. In addition, unconventional cores can be compromised such that their lab measurements which otherwise look perfectly normal, lead to a wrong analysis. Inadvertently, a core can be measured which was ‘reinforced’ by the deposition of drilling fluid. In some cases, the drilling fluid includes the true rock properties.

This paper proposes the design of a system that enables the severely disabled to communicate using their EOG, EMG and EEG signals. The typical approach is to modify the end devices to interpret these signals and to function accordingly [2-5]. But our system proposes an Interpreter System within which these signals are decoded and interfaced with the existing devices. The advantage is that the end devices need not be changed to suit the user, because the logic required for interpretation is within the Interpreter System. Therefore it acts as an interface through which the user can control multiple devices at the same time. Acquisition systems can capture and process EOG, EMG and EEG signals and transmit them to the Interpreter System via the Bluetooth Low Energy module. A communication language using EOG works in combination with EEG and EMG to enable communication. This language will help the users to interface with multiple devices with a limited number of symbols.

Objectives/Scope Though ‘Big Data’ has been a much talked topic in recent years, its potential has not been fully utilized to study rocks for the purpose of improving asset development workflow. Our research has been focused on this topic. Upstream research publications combining imaging; elemental analysis and the mineral compositional information to derive a mineral map have recently started. This is very welcome as both SEM (scanning electron) and Optical Microscopy have tremendous latent potential to assist in reservoir characterization including depositional environment and diagenesis and to develop a more accurate reservoir model. In this study we describe new advanced image analysis that combines both SEM and optical microscopy. Results are used to study rock texture and predict rock fracture behavior. Methods, Procedures, Processes Carbonate and sandstone rock samples were imaged using QEMSCAN (Quantitative Evaluation of Minerals using Scanning Electron Microscope) and optical microscopy analysis. Rock sections were prepared from cores. New digital data processing techniques were devised to extract the information and compute statistics and eventually automate data extraction. Results, Observations, Conclusions The information from image processing such as porosity, grain size, shape, mineral associations, average distance between the neighboring grains, spatial distribution, crack patterns etc. has been used to find correlations between crack propagation and the texture of the rock. Combination of SEM and optical imaging techniques allows one to differentiate between cement and the mineral grains. It is found that the crack pattern is affected by the number of mineral grains per unit area. Higher number of mineral grains per unit area leads to more complex crack pattern which has implications for fraccability. Results show that quantitative microscopy provides a relationship between rock texture and fracture behavior. A new mathematical model is developed to predict the crack length as a function of grain size. Novelty While recently XRD/XRF and elemental composition have been more frequently used by Industry, this study focuses on the importance of accurate, comprehensive and quantitative rock texture characterization. Novel image processing techniques and workflows developed by the authors were used to quantify texture. This work also reinforces the case of using complementary microscopy techniques for more accurate and insightful analysis.