RESERVOIR CHARACTERIZATION OF THE KIZLER NORTH FIELD, LYON CO., KANSAS, USA

The Kizler North Field is near the western flank of the Forest City Basin in Lyon Co., Kansas, USA and produces oil from the Hunton Formation, Viola Formation and Simpson Group reservoirs. The structure strikes NW through the field that is part of a larger wrench fault system. This modern prospect analysis of the Kizler North Field, aids in understanding the reservoir properties of the Hunton Formation, Viola Formation and Simpson Group rocks, the play mechanism of the field, and provides recommendations for additional drilling locations

Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach

Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.

Analysis of Auditory Evoked Potential Signals Using Wavelet Transform and Deep Learning Techniques

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. One of the best ways to solve this problem is early and successful hearing diagnosis using electroencephalogram (EEG). Auditory Evoked Potential (AEP) seems to be a form of EEG signal with an auditory stimulus produced from the cortex of the brain. This study aims to develop an intelligent system of auditory sensation to analyze and evaluate the functional reliability of the hearing to solve these problems based on the AEP response. We create deep learning frameworks to enhance the training process of the deep neural network in order to achieve highly accurate hearing deficit diagnoses. In this study, a publicly available AEP dataset has been used and the responses have been obtained from the five subjects when the subject hears the auditory stimulus in the left or right ear. First, through a wavelet transformation, the raw AEP data is transformed into time-frequency images. Then, to remove lower-level functionality, a pre-trained network is used. Then the labeled images of time-frequency are then used to fine-tune the neural network architecture’s higher levels. On this AEP dataset, we have achieved 92.7% accuracy. The proposed deep CNN architecture provides better outcomes with fewer learnable parameters for hearing loss diagnosis

A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology

The Classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals

Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of EOG based HCI is to control assistive devices using eye movement which can be utilized to rehabilitate the disabled people. In this paper, a novel approach of four classes EOG has been proposed to investigate the possibility of real-life HCI application. A variety of time-domain based EOG features including mean, root mean square (RMS), maximum, variance, minimum, medium, skewness and standard deviation have been explored. The extracted features have been classified by the linear discriminant analysis (LDA) with the classification accuracy of training accuracy (90.43%) and testing accuracy (88.89%). The obtained accuracy is very encouraging to be utilized in HCI technology in the purpose of assisting physically disabled patients. Total 10 participants have been contributed to record EOG data and the range between 21 and 29 years old

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time

Tectonostratigraphy, Structural Styles, and Hydrocarbon Prospectivity of the Rifted-Passive Margins of the Southern Gulf of Mexico and the Atlantic Margin of Morocco

This dissertation addresses the tectonic evolution, structural style, source rock maturity, and hydrocarbon prospectivity of rifted-passive margins of the Gulf of Mexico (GOM) and circum-Atlantic and western Indian Oceans. The datasets for both of these study areas include: 1) pre-stack depth migrated (PSDM) 2D seismic reflection and shipborne potential field data; 2) satellite free-air gravity and magnetic data; and 3) previous geologic cross-sections, offshore seismic reflection profiles, and on- and offshore wells from the oil industry and the International Ocean Drilling Project (IODP). Chapter 2 compiles information from my own study of the southern GOM with previously published information on the structural evolution and similarities of shale- and salt-detached, deepwater passive margin foldbelts (PMFBs) of the Gulf of Mexico, Atlantic Ocean, and western Indian Ocean. Comparison of the thirteen passive margin foldbelts from these margins shows that their shared structural characteristic of linked updip normal faults and downdip folds and thrust faults are driven by gravitational forces resulting from thermal subsidence, onshore cratonic uplift, tectonic oversteepening of the margin, and deltaic depositional loading. Symmetrical detachment folds typically dominate systems associated with salt detachment, whereas shale-detached PMFBs are characterized by imbricate thrusts and more asymmetrical fault-bend and fault-propagation folds. Chapter 3 focuses on the tectonic evolution of the Campeche salt basin in the southern Gulf of Mexico and integrates shipborne magnetic data with 28,612 km of pre-stack, depth-migrated, 2D seismic data to reconstruct the geometry of the top of the Paleozoic crystalline basement and the base of the Jurassic salt body. This mapping better defined the 400-km-long and 40-55-km-wide outer marginal trough, which formed adjacent to the late Jurassic oceanic crust of the central GOM and acts to channel the downslope flow of gravitationally-driven salt of the Campeche passive margin foldbelt. Restoration of seismic lines established the 20.5 km updip extension in the Comalcalco rift. In Chapter 4, I conducted thermal stress modeling along the Campeche salt basin to better understand the spatial variation of present-day maturation of the potential source rocks in the southern Gulf of Mexico and their expelled petroleum volume. The critical feature for the maturation of hydrocarbons is the elongate outer marginal trough described in detail in Chapter 3. My 1D and map-based modeling demonstrate that the more deeply buried, late Jurassic source rocks matured in the late Paleogene to early Neogene and are currently expelling oils into the water column as known from natural oil surface seeps emanating from the seafloor overlying the outer marginal trough. Seismic reflection data show large, salt-related traps are directly connected by faulted pathways to oil kitchens within deep minibasins. Chapter 5 uses the same mapping approach as Chapter 4 to describe an elongate, 80-150-km-wide marginal rift that overlies the zone of continental necking and parallels the modern coastline of Morocco. 1D and map-based thermal maturity modeling show that Jurassic source rocks of the marginal rift are mature for petroleum expulsion along a 400-km length of this rift. Late Cretaceous uplift and erosion of the margin documented in IODP wells are related to Africa-Eurasia convergence across northern Africa and provide an explanation for the observed immaturity of Cretaceous source rocks and the maturity of the underlying Jurassic source rocks.Earth and Atmospheric Sciences, Department o

Wrench faulting and trap breaching: A case study of the Kizler North Field, Lyon County, Kansas, USA

The Kizler North Field in northwest Lyon County, Kansas, is a producing field with structures associated with both uplift of the Ancestral Rockies (Pennsylvanian to early Permian) and reactivation of structures along the Proterozoic midcontinent rift system (MRS), which contributed to the current complex and poorly understood play mechanisms. The Lower Paleozoic dolomitic Simpson Group, Viola Limestone, and “Hunton Group” are the reservoir units within the field. These units have significant vuggy porosity, which is excellent for field potential; however, in places, the reservoir is inhibited by high water saturation. The seismic data show that two late-stage wrench fault events reactivated existing faults. The observed wrench faults exhibit secondary P, R’, and R Riedel shears, which likely resulted from Central Kansas uplift-MRS wrenching. The latest stage event breached reservoir caprock units during post-Mississippian to pre-Desmoinesian time and allowed for hydrocarbon migration out of the reservoirs. Future exploration models of the Kizler North and analog fields should be based on four play concepts: 1) four-way closure with wrench-fault-related traps, 2) structural highs in the Simpson Group and Viola Limestone, 3) thick “Hunton Group,” and 4) presence of a wrench fault adjacent to the well location that generates subtle closure but not directly beneath it, which causes migration out of reservoirs. In settings where complex structural styles are overprinted, particular attention should be paid to the timing of events that may cause breaches of seals in some structures but not others. Mapping the precise location and vertical throw of the reactivated wrench faults using high-resolution seismic data can help reduce the drilling risk in analog systems

Diagenetic Clay Minerals and Their Controls on Reservoir Properties of the Shahbazpur Gas Field (Bengal Basin, Bangladesh)

Clay mineralogy and diagenesis affect the reservoir quality of the Neogene Surma Group in the Hatiya trough of Bengal Basin, Bangladesh. X-ray diffraction and scanning electron microscopic analyses of diagenetic clay minerals from Shahbazpur#2 well reveal that on average illite is the dominant clay mineral (50%), followed by chlorite (24%), kaolinite (23%) and smectite (2.50%). The absence of smectite at Core-2 (3259.80 m to 3269 m) results from the total transformation of smectite to illite owing to burial depth and high K–feldspar. The diagenetic changes are a result of chemical processes such as cementation, chlorite authigenesis, dissolution, alteration and replacement that have significantly affected the reservoir properties. Cementation plays an important role in reducing reservoir properties with pore and fracture filling cement. The relative percentage of illite and smectite minerals (>90% illite in I/S mixed layer) and Kübler index value (0.34° to 0.76° Δ2θ) indicate a diagenetic zone with subsurface temperatures of 120–180 °C in the studied samples. The temperature range determined using clay percentages and the Kübler index as a geothermometer is supported by observed diagenetic features such as quartz overgrowths, smectite to illite transformations and chlorite coatings. The diagenetic features cause variable reservoir porosity and permeability that are critical in planning exploration and development programs of this field or analog fields across the Bengal Basin