"Baylor and Waco have been winners together for over 125 years," Battershell said. According to Clearview International COO Jay Battershell, choosing Waco as the location was no coincidence. "The expansion of Clearview's Texas Star Data Center in Waco is a great opportunity for our students and alumni to learn more about cloud storage and the growth of Baylor's Computer Science program."Ĭlearview first opened this Texas Star Data Center in 2009. "As the Baylor School of Engineering and Computer Science continues to expand our research in the areas of cloud and big data technologies, we are excited for this renovated Tier III Data Center to open its doors in Central Texas," O'Neal said. Dennis O'Neal, Dean of the School of Engineering and Computer Science at Baylor University, will also be in attendance. The celebration will include tours of Baylor's newly built McLane Stadium, as well as Clearview's Waco data center and fiber network access point.ĭr. To celebrate this momentous occasion, Clearview has partnered with the Baylor School of Engineering and Computer Science to host a kick-off event on Friday, Sept. The center is located in the city's Central Business District, at 700 Austin Ave. The facility, a Texas Star Data Center, now boasts an additional 6,000 square feet of critical data center floor space. The permeability data derived from the flow zone index (FZI) analysis revealed the uncertainty of the relative permeability curve end points, adding the benefit of knowing the distribution uncertainty range for initial water saturation, residual oil saturation, and sidewall core saturation, rendering it available for use in the future history-matching process.WACO, TX-(Marketwired - Aug 20, 2014) - Next month, Clearview International will reopen the doors to its newly expanded data center in Waco, Texas. Also, a large difference between upscaled model porosity and log porosity occurred In comparing the static model with real data, the mismatch of well tops and reservoir contact areas in the eastern part of the static model also points to needed structural changes in that static model. Formation test results show no communication between the two field compartments, thus removing the southern compartment from the modeling. The completed preparation of input data and analysis produced new useful information where it previously did not exist, establishing which static model to use. Identified and improved representative static model through Petrel platform QC process Well tops and pay-zone contact areas to compare with the static models. Static models and overall QC was performed in the Petrel platform using log When all input data were prepared, evaluation of the two competing Predictive PVT model, taking into account reservoir fluid behavior with water Generate the new representative equation-of-state model that simulated a Reservoir engineering team coordinated access to the workover history reportsĪnd used the ECLIPSE simulators pressure volume temperature index to Correction and interpretation of MDT modular formationĭynamics tester data verified fluid contacts, pressures, and fluid gradientsįor evaluating communication between the two field compartments. Saturation-Height Modeling (SHM) module, derived the averaged capillary
InĪddition, use of the Leverett-J function, available within the Techlog Relative rock permeability by normalization and denormalization processing. For each facies, these data were then used to derive averaged Special core analysis (SCAL) data to characterize the dolomite and dolomitic The absence of core data in this project, analysis of well logs generated Reservoir properties from well logs, well tests, production data, and simulatedĭata, incorporating this data and results into a unified project dataset.
Software, and the ECLIPSE simulator to manage, develop, and process fluid and Schlumberger recommended using Petrel Reservoir Engineering (RE), OFM Use the Petrel platform and ECLIPSE simulator to manage, develop, and Rock characterization, unrepresentative PVT tables due to water injection,Ĭommingled production in some wells, and unpredictable access to workover There were two competing geologic models, uncertaintyĬoncerning pressure communication between compartments, no core analysis for The type of data available for analysis was
However, severalĬhallenges developed when preparing input data for future model simulations and
The Alamein Petroleum Company sought to develop an accurate fieldĭevelopment plan for the Abu Roash Formation, one of two producing dolomite andĭolomitic limestone reservoirs in Horus field, Egypt. Prepare data and QC model for future reservoir simulation