Gathering data

The model-solving algorithm is responsible for solving the facility location model using the provided input data. The geographical information system is used to represent the modeling results. The representation is created by combining spatial data layers provided by different external data sources. 

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This first information about the reservoir is recorded, as a function of depth, in the form of several columns. Although rather qualitative in many respects, mudlogging is an important data gathering technique. It is of importance as a basis for operational decisions, e.g. at what depth to set casing, or where to core a well. Mudlogging is also cheap, as data is gathered while the normal drilling operations go on. 

The section is divided into four parts, which discuss the common reservoir types from a geological viewpoint, the fluids which are contained within the reservoir, the principal methods of data gathering and the ways in which this data is interpreted. Each section is introduced by pointing out its commercial relevance. 

To derive a reservoir geological model various methods and techniques are employed mainly the analysis of core material, wireline logs, high resolution seismic and outcrop studies. These data gathering techniques are further discussed in Sections 5.3 and 2.2. 

Data gathering in the water column should not be overlooked at the appraisal stage of the field life. Assessing the size and flow properties of the aquifer are essential in predicting the pressure support which may be provided. Sampling of the formation water is necessary to assess the salinity of the water for use in the determination of hydrocarbon saturations. 

The basic data gathering methods are direct methods which allow visual inspection or at least direct measurement of properties, and indirect methods whereby we infer reservoir parameters from a number of measurements taken in a borehole. The main techniques available within these categories are summarised in the following table ... 

This section will look at formation and fluid data gathering before significant amounts of fluid have been produced hence describing how the static reservoir is sampled. Data gathered prior to production provides vital information, used to predict reservoir behaviour under dynamic conditions. Without this baseline data no meaningful reservoir simulation can be carried out. The other major benefit of data gathered at initial reservoir conditions is that pressure and fluid distribution are in equilibrium this is usuaily not the case once production commences. Data gathered at initial conditions is therefore not complicated... 

Mudlogging is another important direct data gathering technique, which was discussed in some detail in Section 2.2, Exploration Methods and Techniques. 

A common objective of a data gathering programme is the acquisition of fluid samples. The detailed composition of oil, gas and water is to some degree reguired by almost every discipline involved in field development and production. 

As discussed in Section 13.2, the technical, fiscal and economic data gathered to construct a project cashflow carry uncertainty. An economic base case is constructed using, for example, the most likely values of production profile and the 50/50 cost estimates, along with the best estimate of future oil prices and the anticipated production agreement and fiscal system. 

Some specific examples of the use of data gathered while monitoring the reservoir will now be discussed. 

The data gathered from the logs and cores of the development wells are used to refine the correlation, and better understand areal and vertical changes in the reservoir quality. Core material may also be used to support log data in determining the residual hydrocarbon saturation left behind in a swept zone (e.g. the residual oil saturation to water flooding). 

Table 10 summarizes sales statistics for the principal segments of the iadustry. The data were gathered by a survey conducted annually by the editors of Ceramic Indust iy magaziae. In 1991, the survey scope was changed from U.S. to a worldwide basis (119,124). Tables 11 and 12 present data gathered by U.S. Department of Commerce agencies. 

A laboratory information management system (LIMS) is a computer or computer network used to automate the acquisition and management of raw analytical data. In its simplest form, it tracks samples and test results through analytical laboratories and provides summaries of the status of these samples and tests. In its most advanced form, the system is interfaced to the laboratory s instmmentation and communication network to allow automation of data gathering, compilation, and reporting. 

Methodology. Practitioners of chemical market research develop iadividual styles and techniques. However, four elements are essential to every useful study defining the problem, data gathering, analysis of data, and presentation of findings. 

Stand-alone computer systems, usually based on a personal computer (PC) or programmable logic controller (PLC), provide a separate computer system for each pilot plant. This allows for economical expansion for new units, separates pilot plants completely for maintenance and troubleshooting, and often has the lowest initial cost. Standardization can be a problem and software control, data gathering, and storage packages can be limited in size, scope, and capabiUty these are usually acceptable trade-offs. 

The maintenance of analytical instmmentation requkes trained personnel and is a time-consuming task (39,40). An additional problem is the necessity of frequentiy checking the caUbration of the analysis instmmentation and recahbrating if requked. Stand-alone data gathering instmmentation, once common in pilot plants, has been vktuaHy replaced in all but the simplest pilot plants by a data gathering computer, usually used for process control as well. 

Virtuady ad new pdot plants wid be computer controded and heavdy automated due to the high cost of operating labor, need for high accuracy and repeatabdity, and ease of data gathering and work-up. Stand-alone computer and programmable logic controder systems wid continue to dominate the market because of their low cost and ease of use. 

A real-time optimization (RTO) system determines set point changes and implements them via the computer control system without intervention from unit operators. The RTO system completes all data transfer, optimization c culations, and set point implementation before unit conditions change and invahdate the computed optimum. In addition, the RTO system should perform all tasks without upsetting plant operations. Several steps are necessaiy for implementation of RTO, including determination of the plant steady state, data gathering and vahdation, updating of model parameters (if necessaiy) to match current operations, calculation of the new (optimized) set points, and the implementation of these set points. 

The technical review team should compare the overall response related to process safety, environmental, health, and quality capability-related items to the data gathered through questionnaires or visits. Does their practice match their policy ... 

Other data, gathered primarily once each day by cooperative observers, consist mostly of temperature and precipitation readings. These are of limited usefulness for air pollution analysis because wind data are generally lacking. 

Currently, most manufacturers use automatic data gathering. Hcrf, pressure signals will be obtained by the use of transducers. Under lliesc circumstances, the transducers should be calibrated per code and certiHed... 

If data gathering is also computer-linked, then the problem is to check out the correctness of the program. Hand calculation of one rated point for How head, efficiency, and horsepower will serve as verification. 

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