Getting Started – First things first! How do you become a student at Illinois Valley Community College? 1. Submit an application for admission. If you have not submitted an application, you can begin the process on-line. Go to http://www.ivcc.edu/apply/ for more information. 2. Complete placement testing. If your ACT English sub score is a 21 or higher, ACT Reading sub score is a 23 or higher and ACT Math sub score is a 22 or higher you are exempt from placement testing. Scores of 24 and 26 in Math will also allow for placement in higher levels of math coursework. If your scores are less than indicated above you may be required to take all or portions of the IVCC placement tests. Individuals will also need to complete a Basic Computer Skills Assessment when taking Placement Tests. For more information go to http://www.ivcc.edu/assessment/placement_test.html. 3. Apply for Financial Aid. 4. Complete this orientation. 5. Meet with a Counselor to schedule your classes and register. Now on with the orientation! back | home | next
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Seismic attribute-assisted interpretation of incised valley fill episodes: A case study of Anadarko Basin Red Fork interval. Yoscel Suarez*, Chesapeake Energy and The University of Oklahoma, USA Kurt J. Marfurt, The University of Oklahoma, USA Mark Falk, Chesapeake Energy, USA Al Warner , Chesapeake Energy, USA Abstract Previous Work Discrimination of valley-fill episodes and their lithology has always posed a challenge for exploration geologists and geophysicists, and the Red Fork sands in the Anadarko Basin do not fall outside of this challenge. The goal of this study is to take a new look at seismic attributes given the considerable well control that has been acquired during the past decade. By using this well understood reservoir as a natural laboratory, we calibrate the response of various attributes to a well-understood incised valley system. The extensive drilling program shows that seismic data has difficulty in distinguishing shale episodes vs. sand episodes, where the ultimate exploration goal is to find productive valley fill sands. In 1998 Lynn Peyton, Rich Bottjer and Greg Partyka published a paper in the Leading Edge describing their use of coherency and spectral decomposition to identify valley fill in the Red Fork interval in the Anadarko Basin. Their work help them identify five valley-fill sequences in order to find optimum reservoir intervals and to reduce exploration risk . Due to the discontinuity of the valley-fill episodes the mapping of such events by using conventional seismic displays is extremely challenging. Figure 3 shows one of the stratigraphic well cross-section presented by Peyton et al where the discontinuities of this complex are evident. Figure 4 shows a seismic profile that parallels the wells cross-section highlighting the same stages. The seismic section is flattened in the Novi. Since original work done in 1998 both seismic attributes and seismic geomorphology have undergone rapid advancement. The findings of this work will be applicable to nearby active areas as well as other intervals in the area that exhibit the same challenges. Using Peyton et al’s (1998) work as a starting point we generated similar displays of conventional seismic profiles and well x-sections that will become the bases of our research efforts. Figure 8 shows the geometry and extents of the different episodes of the Red Fork incised valley system based on well data interpretation and conventional seismic displays. This map will be compared to the different seismic attributes to calibrate their response. Figure 9 (a,b) show couple of well x-sections and their corresponding seismic profiles that supported the valley-fill stages map in Figure 8. Seismic attributes have undergone rapid development since the mid 1990s. In lieu of the horizon-based spectral decomposition based on the discrete Fourier transform, we use volumetric-based spectral decomposition based on matched pursuit and wavelet transforms (e.g. Liu and Marfurt,2007) . Other edge-sensitive attributes include more modern implementations of coherence, long-wavelength Sobel filters, and amplitude gradients. Figure 10 shows a horizon slice at the Red Fork level. Note that on conventional data the channel complex is identifiable. However, the use of seismic attributes may help delineate in more detail the different episodes within the same fluvial system and better define channel geomorphology. We will compare different edge detection algorithms and the advantages and disadvantages that each of them provides to the interpreter. Also, matching pursuit spectral decomposition results will be presented as well as combinations of Relative Acoustic Impedance and semblance that provide helpful information in the interpretation of this dataset. The surveys are located in west central Oklahoma. They were shot by Amoco from 19931996 and later merged into a 136 sq.mi. survey. In 1998, Chesapeake acquired many of Amoco’s Mid-continent properties including those discussed by Peyton et al. (1998). In this study we present alternative seismic attribute-assisted interpretation workflows that show the potential information that each of the geometric and amplitude-based attributes offer to the interpreter when dealing with Red Fork valley-fill episodes in the Anadarko Basin. It is important to mention that one of the biggest challenges of this dataset is the acquisition footprint, which contaminates the data and limits the resolution of some of the seismic attributes. Geological Framework Methodology A Figure 3. Stratigraphic cross-section Red Fork valley –fill complex Figure 4. Seismic profile associated to the prior crosssection. Flattened in the Novi interval By generating horizon slices in the coherency volume they were able to identify and delineate the main geometries of the incised valley (Figure 5). The event used to generated the horizon slice is the Skinner Lime above the Red Fork interval. A’ The Pennsylvanian incised valley sequence associated with the Red Fork interval has, throughout most of its extent, three major events or facies (Phase I, II, and III) which can be differentiated by log signatures, production characteristics, and gross geometry. Two additional events (Phase IV and V) are present in the eastern and northeastern headward portion of the valley, also recognizable by log signature and gross geometry. Phase II Phase III Phase V Figure 8. Red Fork incised valley geometries and valley-fill episodes The multi phase events of the Upper Red Fork Valley system were most likely caused by repeated sea level changes resulting from Pennsylvania glacial events that were probably related to the Milankovitch astronomical cycles including the changing tilt of the earth’s axis and eccentricity of the earth’s elliptical orbit. Phase I is the earliest valley event and Phase II generally has a much wider represents the narrow, initial downcutting of the valley sequence. Where present (a considerable portion of Phase I has been eroded by later events), the rocks are generally poorly correlative shales, silts, and tight sandstones overlying a basal “lag” deposit. areal distribution (up to four miles) with a variety of valley fill facies deposited which record a period of valley widening and maturation. Logs over Phase II rocks illustrate a classic fining upward pattern and shale resistivities of 10 or more ohms. Phase III rocks record the last major incisement within the valley and occur within a narrow (0.25-.05 mile wide) steep walled system that is correlative for 70 miles. This rejuvenated channel actually represents the final position of the Phase II river before base level was lowered and renewed downcutting began. Phase III reservoirs are primarily thick, blocky, porous sands at the base of the sequence that have been backfilled, reworked, and overlain by low resistivity marine shales deposited by a major transgression which drowned the valley sequence. Figure 5. Coherency horizon slice at the Red Fork level Phase V the last event before the transgression that deposited the Pink. It’s primary significance is that it either partially or completely eroded much of the Phase III Valley event. Phase V rocks are poorly developed, non productive sand and shales which also have a characteristic log signature. end of Phase III marine shale deposition. Phase IV rocks are characterized by thin, tight, interbedded sands and shales with a coal or coaly shale near the base. This facies is interpreted as an elongated lagoon/ coal swamp or possibly bay head delta as it often extends beyond the confines of the deeper valley. The Induction log signature is a very distinct “serrated” pattern with a “hot” gamma ray near the base identifying the coal or coaly shale. Pink Lime In their workflow they also estimated the spectral decomposition. They found that the 36 Hz component best represented the different valley-fill stages (Figure 6). By combining the well-data with the information from the seismic attributes they were able to delineate the extents of the different valley –fill episodes (Figure 7) and generate and integrated interpretation of the system. Lower Red Fork II III II Middle Red Fork V a) Figure 9. a) Red Fork stratigraphic cross-section. b) Seismic profile showing the stratigraphic interpretation derived from the well data Phase IV records a modest regression at the The geological framework summary is courtesy of Al Warner. Senior Geologist at Chesapeake Energy Figure 10. Conventional seismic horizon slice at the Red Fork level. The channel discernible although signal/noise ratio is affected by acquisition footprint Figure 6. Spectral decomposition (36 Hz) horizon slice at the Red Fork level Figure 7. Spectral decomposition (36 Hz) horizon slice at the Red Fork level with interpretation. III b) II V
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PLACEMENT HOW? Placement Placement Test Test Transfer Transfer Credits Credits SAT SAT or or ACT ACT Scores Scores High High School School AP AP Scores Scores (Mathematics) (Mathematics) WHAT TO DO? Take Take placement placement test test on on any any campus campus at at Testing Testing Center. Center. Accuplacer Accuplacer MATH MATH can can be be taken taken twice. twice. Students Students who who wish wish higher higher placement placement must must then then “appeal”. “appeal”. Appeal Appeal is is request to take a pencil-and-paper type test on material from the course they request to take a pencil-and-paper type test on material from the course they placed placed into. into. Bring Bring transcript transcript showing showing general general education education math math credits credits from from another another college college or or university university to to CCBC CCBC for for evaluation. evaluation. If If completed completed in in Maryland, Maryland, the the highest highest level level developmental math course, Intermediate Algebra can be used for placement. developmental math course, Intermediate Algebra can be used for placement. Placement Placement by by SAT SAT MATH MATH score score of of 500 500 or or higher higher (or (or by by ACT ACT MATH MATH score score 21 21 or or higher) places the student into an entry-level general education math course higher) places the student into an entry-level general education math course (MATH (MATH 111, 111, 125, 125, 131/2/3, 131/2/3, 135, 135, 163). 163). For For higher higher placement, placement, the the student student must must take take Accuplacer MATH. MATH. Accuplacer Students Students with with documentation documentation of of AP AP math math scores scores of of 3, 3, 4, 4, or or 5 5 from from high high school school can can be awarded college credit and placement according to this chart. be awarded college credit and placement according to this chart.
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5. Mean projections and mean student scores are calculated. Student Projection1 Student Score 1 Student Projection 2 Student Score 2 Student Projection 3 Student Score 3 Student Projection 4 Student Score 4 Student Projection 5 Your School Student Score 5 Student Projection 6 Student Score 6 Student Projection 7 Student Score 7 Student Projection 8 Student Score 8 Student Projection 9 Student Score 9 Student Projection 10 Student Score 10 Student Projection 11 Student Score 11 Student Projection 12 Student Score 12 Student Projection 13 Student Score 13 Student Projection 14 Student Score 14 Student Projection 15 Student Score 15 Student Projection 16 Student Score 16 Student Projection 17 Student Score 17 Student Projection 18 Student Score 18 Student Projection 19 Student Score 19 Student Projection 20 Student Score 20 Mean Projected Score Mean Student Score Copyright © 2003. Battelle for Kids
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Schreiber, Schwöbbermeyer [12] proposed flexible pattern finder (FPF) in a system Mavisto.[23] It exploits downward closure , applicable for frequency concepts F2 and F3. The downward closure property asserts that the frequency for sub-graphs decrease monotonically by increasing the size of sub-graphs; but it does not hold necessarily for frequency concept F1. FPF is based on a pattern tree (see figure) consisting of nodes that represents different graphs (or patterns), where the parent is a sub-graph of its children nodes; i.e., corresp. graph of each pattern tree’s node is expanded by adding a new edge of its parent node. At first, FPF enumerates and maintains info of all matches of a sub-graph at the root of the pattern tree. Then builds child nodes of previous node by adding 1 edge supported by a matching edge in target graph, tries to expand all of previous info about matches to the new sub-graph (child node).[In next step, it decides whether the frequency of the current pattern is lower than a predefined threshold or not. If it is lower and if downward closure holds, FPF can abandon that path and not traverse further in this part of the tree; as a result, unnecessary computation is avoided. This procedure is continued until there is no remaining path to traverse. It does not consider infrequent sub-graphs and tries to finish the enumeration process as soon as possible; therefore, it only spends time for promising nodes in the pattern tree and discards all other nodes. As an added bonus, the pattern tree notion permits FPF to be implemented and executed in a parallel manner since it is possible to traverse each path of the pattern tree independently. But, FPF is most useful for frequency concepts F2 and F3, because downward closure is not applicable to F1. Still the pattern tree is still practical for F1 if the algorithm runs in parallel. It has no limitation on motif size, which makes it more amenable to improvements. ESU (FANMOD) Sampling bias of Kashtan et al. [9] provided great impetus for designing better algs for NM discovery, Even after weighting scheme, this method imposed an undesired overhead on the running time as well a more complicated impl. It supports visual options and is time efficient. But it doesn’t allow searching for motifs of size 9. Wernicke [10] RAND-ESU is better than jfinder, based on the exact enumeration algorithm ESU, has been implemented as an app called FANMOD.[10] Rand-esu is a discovery alg applicable for both directed and undirected networks. It effectively exploits an unbiased node sampling, and prevents overcounting sub-graphs. RAND-ESU uses DIRECT for determining sub-graph significance instead of an ensemble of random networks as a Null-model. DIRECT estimates sub-graph # w/oexplicitly generating random networks.[10] Empirically, DIRECT is more efficient than random network ensemble for sub-graphs with a very low concentration. But classical Null-model is faster than DIRECT for highly concentrated sub-graphs.[3][10] ESU alg: We show how this exact algorithm can be modified efficiently to RAND-ESU that estimates sub-graphs concentrations. The algorithms ESU and RAND-ESU are fairly simple, and hence easy to implement. ESU first finds the set of all induced sub-graphs of size k, let Sk be this set. ESU can be implemented as a recursive function; the running of this function can be displayed as a tree-like structure of depth k, called the ESU-Tree (see figure). Each of the ESU-Tree nodes indicate the status of the recursive function that entails two consecutive sets SUB and EXT. SUB refers to nodes in the target network that are adjacent and establish a partial sub-graph of size |SUB|≤k. If |SUB|=k, alg has found induced complete sub-graph, Sk=SUB ∪Sk. If |SUB|v} graphs of size 3 in the target graph. call ExtendSubgraph({v}, VExtension, v) endfor Leaves: set S3 or all of size-3 induced sub-graphs of the target graph (a). ESUtree nodes incl 2 adjoining sets: adjacent ExtendSubgraph(VSubgraph, VExtension, v) nodes called SUB and EXT=all adjacent if |VSubG|=k output G[VSubG] return 1 SUB node and where their numerical While VExt≠∅ do Remove arbitrary vertex w from VExt labels > SUB nodes labels. EXT set is VExtension′←VExtension∪{u∈Nexcl(w,VSubgraph)|u>v} utilized by the alg to expand a SUB set call ExtendSubgraph(VSubgraph ∪ {w}, VExtension′, v) until it reaches a desired size placed at return lowest level of ESU-Tree (or its leaves).
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FAFSA (Federal Financial Aid) Complete your FAFSA (Free Application for Federal Financial Aid) each year. • Use 2016 tax returns for 2018-2019 FAFSA. Be sure to use the IRS Retrieval Tool. If you are selected for Federal Verification, your tax transcript will have already been provided to the college which will save time and effort. • Bergen Community College must be listed as one of your college choices. The school code is #004736. • If you need help, attend one of the 2018-2019 FAFSA Workshops at Bergen Community College, or view the “ 7 Easy Steps to the FAFSA” Video Tutorial. • Check Bergen Community College to see the Office of Financial Aid emailed you via your student email advising you were selected for Federal Verification. If selected, submit all documents required by the Office of Financial Aid via scanning process. You should receive an email confirmation within an hour advising that your documents were successfully scanned. • Check in Correspondence/My Documents in your Bergen Community College student portal to be sure you aren’t missing any documents. If your FAFSA is not listed in My Documents, the financial aid review process cannot begin. • Check Bergen Community College student email and student portal to be sure the Office of Financial Aid either sent you an award letter or an email stating you do not qualify for Financial Aid which signifies your file is complete. • Access How to be Sure Your Federal Financial Aid Review is Complete. 10
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Financial Information Financial Aid Things to Know Did you know… That you can lose your federal financial aid if you do not meet Standards of Progress-ie. You must pass 66.6% of all college courses ever taken, and you cannot be academically suspended. That you can appeal your loss of financial aid provided you have documentation of extenuating circumstances such as severe illness or change of program. However, you should make the Appeal immediately upon receipt of your letter so you can be sure to get an answer in a timely manner. Write your appeal using the form provided by the Financial Aid Office, have a Counselor review it (required) and submit it to the IVCC Director of Financial Aid. That you must re-apply for financial aid Every Year - apply in the Spring for the upcoming Fall through Summer terms. Be sure to visit with the IVCC Financial Aid Counselors for more help and information. back | home | next
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