Evaluation of geological/geotechnical geostatistical models for tunneling applications

Author(s):  
R. Gangrade ◽  
W. Trainor-Guitton ◽  
M. Mooney ◽  
J. Grasmick
2021 ◽  
Vol 121 ◽  
pp. 107204
Author(s):  
Katarzyna Bzdęga ◽  
Adrian Zarychta ◽  
Alina Urbisz ◽  
Sylwia Szporak-Wasilewska ◽  
Michał Ludynia ◽  
...  

2020 ◽  
Author(s):  
Ourohiré Millogo ◽  
Jean Edouard Odilon Doamba ◽  
Ali Sié ◽  
Juerg Utzinger ◽  
penelope vounatsou

Abstract Abstract Background: The Service Availability and Readiness Assessment (SARA) surveys generate data on the readiness of health facility services. We constructed a readiness index related to malaria services and determined the association between health facility malaria readiness and malaria mortality in children under the age of 5 years in Burkina Faso. Methods: Data on inpatients visits and malaria-related deaths in under 5-year-old children were extracted from the national Health Management Information System (HMIS) in Burkina Faso. Bayesian geostatistical models with variable selection were fitted to malaria mortality data. The most important facility readiness indicators related to general and malaria-specific services were determined. Multiple correspondence analysis (MCA) was used to construct a composite facility readiness score based on multiple factorial axes. The analysis was carried out separately for 112 medical centres and 546 peripheral health centres. Results: Malaria mortality rate in medical centres was 4.8 times higher than that of peripheral health centres (3.46% vs. 0.72%, p<0.0001). Essential medicines was the domain with the lowest readiness (only 0.1% of medical centres and 0% of peripheral health centres had the whole set of tracer items of essential medicines). Basic equipment readiness was the highest. The composite readiness score explained 30% and 53% of the original set of items for medical centres and peripheral health centres, respectively. Mortality rate ratio (MRR) was by 59% (MRR = 0.41, 95% Bayesian credible interval (BCI): 0.19-0.91) lower in the high readiness group of peripheral health centres, compared to the low readiness group. Medical centres readiness was not related to malaria mortality. The geographical distribution of malaria mortality rate indicate that regions with health facilities with high readiness show lower mortality rates. Conclusion: Performant health services in Burkina Faso are associated with lower malaria mortality rates. Health system readiness should be strengthened in the regions of Sahel, Sud-Ouest and Boucle du Mouhoun. Emphasis should be given to improving the management of essential medicines and to reducing delays of emergency transportation between the different levels of the health system. Keywords: Bayesian geostatistical models, Burkina Faso, Composite readiness index, Malaria, Service Availability and Readiness Assessment


Engineering ◽  
2011 ◽  
Vol 03 (09) ◽  
pp. 886-894 ◽  
Author(s):  
Pijush Samui ◽  
Thallak G. Sitharam

2015 ◽  
Vol 26 (4) ◽  
pp. 243-254 ◽  
Author(s):  
Ephraim M. Hanks ◽  
Erin M. Schliep ◽  
Mevin B. Hooten ◽  
Jennifer A. Hoeting

2002 ◽  
Vol 5 (02) ◽  
pp. 135-145 ◽  
Author(s):  
G.R. King ◽  
W. David ◽  
T. Tokar ◽  
W. Pape ◽  
S.K. Newton ◽  
...  

Summary This paper discusses the integration of dynamic reservoir data at the flow-unit scale into the reservoir management and reservoir simulation efforts of the Takula field. The Takula field is currently the most prolific oil field in the Republic of Angola. Introduction The Takula field is the largest producing oil field in the Republic of Angola in terms of cumulative oil production. It is situated in the Block 0 Concession of the Angolan province of Cabinda. It is located approximately 25 miles offshore in water depths ranging from 170 to 215 ft. The field consists of seven stacked, Cretaceous reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This paper discusses the data acquisition and integration for this reservoir only. The reservoir was discovered in January 1980 with Well 57- 02X. Primary production from the reservoir began in December 1982. The reservoir was placed on a peripheral waterflood in December 1990. Currently, the Upper Vermelha reservoir accounts for approximately 75% of the production from the field. Sound management of mature waterfloods has been identified as a key to maximizing the ultimate recovery and delivering the highest value from the Block 0 Asset.1 Therefore, the objective of the simulation effort was to develop a tool for strategic and dayto- day reservoir management with the intent of managing and optimizing production on a flow-unit basis. Typical day-to-day management activities include designing workovers, identifying new well locations, optimizing injection well profiles, and optimizing sweep efficiencies. To perform these activities, decisions must be made at the scale of the individual flow units. In general, fine-grid geostatistical models are developed from static data, such as openhole log data and core data. Recent developments in reservoir characterization have allowed for the incorporation of some dynamic data, such as pressure-transient data and 4D seismic data, into the geostatistical models. Unfortunately, pressure-transient data are acquired at a test-interval scale (there are typically 3 to 4 test intervals per well, depending on the ability to isolate different zones mechanically in the wellbore), while seismic data are acquired at the reservoir scale. The reservoir surveillance program in the Takula field routinely acquires data at the flow-unit scale. These data include openhole log and wireline pressure data from newly drilled wells and casedhole log and production log (PLT) data from producing/injecting wells. Because of the time-lapse nature of cased-hole log and PLT data, they represent dynamic reservoir data at the flow-unit scale. To achieve the objectives of the modeling effort and optimize production on a flow-unit basis, these dynamic data must be incorporated into the simulation model at the appropriate scale. When these data are incorporated into a simulation model, it is typically done during the history match. There are, however, instances when these data are incorporated during other phases of the study. The objective of this paper, therefore, is to discuss the methods used to integrate the dynamic reservoir data acquired at the flow-unit scale into the Upper Vermelha reservoir simulation model. Reservoir Geology The geology of the Takula field is described in detail in Ref. 2. The aspects of the reservoir geology that are pertinent to this paper are elaborated in this section. Reservoir Stratigraphy. The Takula field consists of seven stacked reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This reservoir contains an undersaturated, 33°API crude oil. For reservoir management purposes, 36 marker surfaces have been identified in the reservoir. Flow units were then identified as reservoir units separated by areally pervasive vertical flow barriers (nonreservoir rock). This resulted in the identification of 20 flow units. The thickness of these flow units ranges from 5 to 15 ft. Reservoir Structure. The reservoir structure is a faulted anticline that is interpreted to be the result of regional salt tectonics. Closure to the reservoir is provided by faults on the southwestern and northern flanks of the structure and by an oil/water contact (OWC) on the eastern, western, and southern flanks of the structure. A structure map of the reservoir is presented in Fig. 1. Data Acquisition in the Takula Field Openhole Log Program. Most original development wells were logged with a basic log suite of resistivity/gamma ray and density/ neutron logs. In addition, the vertical wells drilled from each well jacket were logged with a sonic log and, occasionally, velocity surveys. All wells drilled after 1993 were logged with long spacing sonic and spectral gamma ray logs. In many wells drilled after December 1997, carbon/oxygen (C/O) logs have been run in open hole to distinguish between formation and injected water.3 A few recent wells have been logged with nuclear magnetic resonance (NMR) logs. The NMR log data, when integrated with data from other logs, have been of value in distinguishing free water from bound water, formation water from injection water, and reservoir rock from nonreservoir rock.


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