ASSUMPTIONS. When analyzing such a large data set, the effect of measurement uncertainties and random variations in the data measured tends to be minimized. In the absence of smaller variations, the larger variations become more conspicuous and relatable to the actual events. Authors also observed this benefit of “aggregate analysis” of data [2,3]. However, instrument and measurement bias will not be minimized when using a large data set, and unless corrected, can lead to erroneous conclusions. The old adage of “garbage in – garbage out” is just as true here as with any computational tool. Data-points generated by these tools are purely mathematical with no regard to the "relative location" or "weight" of each individual point. However, logical selection of individual xxxxx to form data-groups will produce meaningful results. When analyzing data using this kind of mathematical "averaging," and using "un-corrected" data, conclusions should be general and relative rather than absolute, unless the user can tie these relative conclusions to some corrected data points. Throughout this paper, wellhead data are used without any correction as to the downhole reservoir conditions. This is mainly because only publicly available data were used. Even if other information were used, it may not have significantly added to the data quality due to many uncertainties [4]. These include: influence of heat losses as steam travels though the borehole, production rate, cross-ties to other xxxxx, and placement of recording gauges. At The Geysers, pressure data typically comes from transmitters that are part of the flow meter, most of which are downstream of the flow control valve. Therefore, recorded pressures are influenced by factors other than reservoir performance. Pipeline frictional losses, other xxxxx (most xxxxx are cross- tied), and power plant inlet pressures can all influence the recorded pressure. The objective of this paper is not to provide an interpretation of The Geysers data or future forecasting, but to present some examples of using these data-reduction tools with real life data.
