The preferred approach for determining net pay in laminated sediments of fluvial and turbidite formations is to delineate sand layers from borehole image data. New image
interpretation software, Net2Gross, has been developed to estimate the sand and pay counts within the subsurface sedimentary sequence logged by XRMI™ X-tended range imager tool or OMRI™ oil mud imager tool. The software exploits the XRMI and OMRI tool’s ability to resolve thin laminations and sedimentary structures. It applies threshold techniques to the pre-processed high resolution XRMI/ OMRI image and constructs secondary images for sand and pay. The analyst retains the flexibility to calibrate these images to the gamma ray and porosity logs using the cumulative distributions from all the logs to determine valid threshold values for the images. The software also generates cumulative sand and pay counts versus depth. An R-sand interpretation is also available by combining image data with triple combo data. This provides quantitative water
saturation in laminated and dispersed shale environments. The sand image is constructed by applying an upper and lower threshold to the conductivity amplitude image, after calibration of this image to the neutron, density and gamma ray logs using the cumulative distributions from the logs and image data to determine valid threshold values. Pixels lying between the lower and upper threshold values, and greater than an analyst-specified cutoff are classified as sand. Sand pixels are then upgraded to pay if all of the following conditions are satisfied: the pixel's image conductivity is below a specified threshold, porosity greater than a threshold depth proximal to the pixel exists, and deep resistivity greater than a threshold depth proximal to the pixel exists. Finally, cumulative sand and pay counts versus depth are constructed by simply counting the sand and pay pixels.
In Track 1, the sand image is presented as a binary image, black for shale and white for sand. Track 2 presents the pay image in which sands interpreted to be pay are assigned the color red.
Features
• High-resolution net sand and net pay images and curves • Cumulative net sand and net pay curve
• Logic to prevent interpretation of tight streaks as pay • Interactive histogram based calibration of logging curves • Accurate sand and net pay counts in laminated
• Better agreement between core and log, net sand, and pay
• Combines image data and triple combo for an R-sand interpretation which provides water saturation for both laminated and dispersed shales
Reservoir Evaluation Services
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ImagePerm
ImagePerm is an image based approach providing a high- resolution porosity and permeability curve as well as a high- resolution porosity image and histograms. In addition, it provides a high resolution secondary porosity curve, which is useful for interpretation in the presence of vugs and fractures.
The basic approach is to calibrate the image data to image porosity using filtering techniques. The image data is averaged over a moving window, and a transform is constructed which calibrates the average image data to porosity. This transform is then applied to the “pixel-by- pixel” image data and a moving adjustment for bias is made. The final result is shown in Track 5, which shows the EMIP (or XRMI™ X-tended range micro-imager tool) porosity image scaled 0 to .3. Track 4 compares the total porosity PHIT from the neutron density logs (lazy black curve) with the image porosity averaged around the borehole (red curve) at each depth. It can be seen the calibration is correct and the resolution is improved for all the tight, low porosity streaks. A porosity histogram of the image data as shown in Track 6 is used to aid in the interpretation and detection of vuggy porosity. Secondary porosity should manifest itself in the histogram being bimodal with the highest porosity mode corresponding to secondary porosity. Given each image porosity histogram, the cumulative distribution can be computed and displayed. In particular, the cumulative distribution in Track 3 shows in red the variation in porosity of those 20% of the samples having the highest porosity. Without any sonic or core data, for illustrative purposes, these samples were assumed to be secondary porosity. This constant fraction is converted to a volume and displayed in Track 4 as the gray shaded portion of the display.
This implementation is intended to support a high- resolution prediction of permeability for carbonates. The Jennings-Lucia model which relates the porosity
permeability transform to rock type has been implemented. One obtains rock type from looking at core data, or by calibration to core permeability. Track 2 shows the permeability from primary porosity as cyan, and from secondary porosity as shaded. The predicted permeability can either decrease or increase with secondary porosity, dependent upon the model selected.
Features
• High resolution image porosity curve and image • High resolution image secondary porosity curve
• High resolution micro porosity from MRIL® tool calibration
• Image depth based histograms for rock facies interpretation
• High resolution intergranular permeability • Permeabilty correction for secondary porosity • Rock type based high-resolution permeability
• Describes porosity and permeability in vuggy carbonate facies
• Helps identify thief zones in vuggy formations, thus aiding in well completion
• Helps identify productive zones in carbonates • Better agreement between core and log, permeability,