Data literacy is slowly becoming a more prominent feature of contemporary society. Some argue that people need to obtain new competencies to mitigate and engage with the multiplicity of ways in which they are affected by data. Literacy as such is positioned as a pathway towards empowerment, enable people to make informed choices about their data environment and increasing their ability to actively participate in the discussion that determines the socio-technical systems that will impact their lives. In this article, I will argue that we need to account for the externalities that emerge from the mere act of centring data in a literacy approach and unpack the assumptions that underpin the concept. To advance the argument that data literacy needs to be (re)politicize, both in terms of the perceived competencies need in a data society and the 'neutrality of the practice in itself. To ensure that the audience will have more thoughtful and actionable pathways forward data literacy should learn from other disciplines that have a more thorough analysis of dismantling power structures, engaging with inequality and encouraging political participation.
Production data is one of the most abundant sources of data available about the reservoir and the well behavior. Several authors have shown how the cross correlation between pairs of wells can provide information about the flow path in the reservoir. The calculation of the cross correlation itself has often proved to be problematic. This is due to the non-linear and non-stationary nature of the inter-well relationship. The inter-well relationship is a function of the boundary conditions imposed by the wells themselves and the reservoir properties. The Wavelet transformation is a new tool, which unlike the Fourier transform, allows for a non-stationary treatment of the data. This opens new possibilities with respect to a robust cross correlation between wells and the use of these data to more consistently determine the cause of the well behavior and its influence on surrounding wells. This paper presents a brief introduction into the use of the Wavelet transformation to decompose the production data into a combination of their frequency (details) and smoothed (scaled) components. The frequency components are then used for the analysis of the inter-well relationship. The time localizing ability of the transform is exploited to generate a robust and time dependent cross correlation between pairs of wells, using traditional cross correlation routines such as the Spearman Rank correlation. This cross correlation can then be used to estimate the degree of well interference, preferential flow paths and the existence of flow barriers. The proposed method is validated using data from the North Robertson Field in West Texas. This field has been under waterflood since 1987. For additional information regarding the waterflood, please see Reference 19. Introduction Production data represent a source of information about the dynamic boundary conditions as well as the static reservoir properties. The in-situ flow process depends upon both the reservoir properties and the boundary conditions in the form of mass and pressure transfer. In a water flood the injection wells provide a dynamic boundary condition. Thus, if we view the reservoir in a simplistic manner (Fig. 1), we can assume that the production rates and pressures are a function of the combination of both the injection rates and the reservoir properties. This allows us to make certain assumptions with respect to the information present in the production data. Production data are by nature dynamic and represents a composite of many different events, such as well control, reservoir decline and near wellbore damage, as well as the influence from nearby wells. By examining the production data, we find that it is often difficult to establish a clear and consistent patterns between pairs of wells. Figure 2 shows an example of the production rate as a function of time plotted along with four surrounding well injection rates. The data are taken from the North Robertson Field in West Texas. For reference the well locations are shown in Figure 3. It is not easy to calculate a cross correlation between a pair of wells. The cross correlation between a pair of wells tends to be nonlinear, thus, non-parametric cross correlation schemes such as the Spearman rank correlation has seen some success. The response between the wells can contain a time lag, in addition the cross correlation itself is also a function of time. The production data therefore represents a non-stationary signal, which require special treatment. Due to the pressure superposition among the wells, a direct and constant relationship between the wells will therefore not be easily detectable. A relatively strong change in the injection rate is required to generate enough of a change in the nearby producers to detect a significant correlation. This nonstationarity causes a time dependency in the cross correlation between the wells, thus, regular cross correlation techniques tend to fail. The Wavelet transform is one method to break down the data into its frequency spectra and still retain the time dependency. P. 323^
Reservoir property interpolation and estimation has traditionally been a stationary process ignoring the dynamic information available from production data. A proper analysis of these data can provide an estimate of the boundary conditions controlling the flow. A cross correlation between a pair of wells provide a measure of the inter-well relationship or the association between the two wells. This measure of association can be utilized to provide a better estimate of the correct search neighborhood and as a tool to introduce an element of non-stationarity into the reservoir property estimation process. While any interpolation routine can be used when the correct search neighborhood is defined, it is possible to incorporate the cross correlation coefficients directly into the interpolation routine to generate an experimental variogram model. However, while the cross correlation approach produces a matrix describing the spatial association between the sampled locations, this does not guarantee an invertible matrix when applied in a kriging like system. To overcome this problem, a distance weighted interpolation of the reservoir properties is used where the cross correlation coefficients provide the appropriate measure of association without the strict assumption of stationarity. The result is a better structural definition using a simpler interpolation system without the need of a variogram This is a rather unique approach utilizing the information about the flow structure found in the production data and introducing an element of non-stationary element into the reservoir property estimation. Introduction Traditional geostatistics apply a stationary approach to data interpolation. The reservoir properties are interpolated between measured data points under the assumption that all of the data belong to the same stationary data set. The samples obtained are often sparse and not randomly sampled, but rather sampled at the locations with the highest expectancy of good reservoir properties. This can lead to a too optimistic and continuos reservoir description. The assumptions about stationarity in our data set are based on our limited knowledge about the continuity and extent of the reservoir from static measurements. A source of information often excluded in this process is the dynamic information available from wells in the form of pressure and rate measurements. These dynamic data contain information about the extent and the continuity of the reservoir. Production data represent a sampling from the underlying flow process. We would like to be able to utilize these data to describe and characterize the important features of the flow process, there by making a prediction of the controlling reservoir properties. By combining the dynamic and the stationary information it is possible to add an element of non-stationarity to the interpolation. One way of adding these non-stationarity data is through a better definition of the correct search neighborhood for an otherwise stationary interpolation process. If we look at a regular stationary interpolation process like kriging, it can easily be seen that this represents an averaging which honors the measured data, but which creates too much smoothness and continuity in the interpolated estimates. This is common for most interpolation methods and is caused by a lack of information about the actual variations between the measured data points. This is also a weakness of stochastic methods, thus, common for all existing methods are a lack of information about the extent of the stationarity. For a good interpolation of the data it therefore becomes critical to select the appropriate sub sets for interpolation. Production data provides a measure of the in-situ flow process and the inter-well relationships. P. 131^
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