Unconventional shale gas reservoirs are known for low porosity, low matrix permeability, the lack of an obvious seal or trap, large regional extent, and, in most areas, are believed to be highly heterogeneous in nature. As a result, it is common practice to confirm the reservoir thickness, evaluate the shale gas rock properties and to determine horizontal shale gas targets using vertical or pilot offset wells. Horizontal wells are then drilled and stimulated to maximize reservoir exposure and enhance inflow production performance.
Taking advantage of the high gamma ray activity found in most shale plays, a majority of horizontal wells are steered to stay within the defined target window using a non-azimuthal, averaged gamma ray measurement only. By relying on a single measurement, there is no fall back when the interpretation presents several possible scenarios. Additionally, a non conclusive interpretation will negatively impact the efforts of optimizing the learning curve across a field.
Resistivity measurements complement gamma ray data as they provide an extra data set for correlation. However, azimuthal images from density measurements acquired in real time can offer structural dip authentication along the well trajectory to provide a higher level of accuracy to the modeled structure. By having a validated structural model, a higher level of confidence in real-time steering decisions can be gained. An accurate structural model is also an effective tool to aid completion designs, correlate formation properties, refine target delineation and provide a foundation for evaluating production logs and microseismic observations.
The main objective of this paper is to demonstrate how structural modeling using only gamma ray in horizontal wells can lead to non-unique solutions that can be a potential cause of inconsistent reservoir interpretations and varied production, not only between hydraulic fracturing stages but also from well to well. Having sufficient measurements for formation evaluation, drilling and production results can be better understood and applied to enhance target selection, followed by accurate well placement within the selected target structure. This level of well placement accuracy will deliver consistent production results and provide a common platform for evaluating completion practices.
The data and materials presented in this paper showcase a case study from the Woodford shale. A common practice in shale reservoirs is to use a gamma ray log from an offset vertical well and apply to horizontal well correlations. The subject reservoir is around 190 ft in vertical thickness (Fig. 1). The overlying and underlying boundaries baseline at 150 GAPI and 30 GAPI respectively while the baseline within the referenced reservoir ranges from 350 GAPI to 400 GAPI with prominent gamma ray peaks that are 700 GAPI or more within it. These high gamma ray markers consistently run laterally through the subject field as verified from vertical offset wells. Resistivity in this reservoir is around 200 ohm-m, with significant changes only at the boundaries where resistivity drops to 20 ohm-m at the top and 40 ohm-m at the base.
The centerline in Figure 1 is a common target for landing horizontal wells in the reservoir. The planned objective calls for the subject lateral to be landed and positioned close to the centerline of the gross reservoir interval. This is achieved by geosteering along a high gamma ray marker while drilling the target well. Using seismic interpretation, the planned lateral would cross the centerline and follow a syncline into a short incline to encounter a second syncline prior to an opposing incline (Fig. 2). After drilling, the completion plan is to geometrically divide the lateral into nine intervals or stages for hydraulic fracturing. Hydraulic fracturing is anticipated to propagate effectively above and below the said centerline.
The well was drilled and landed as interpreted from seismic data. Using the gamma ray for correlation, the well trajectory was geosteered generally along the formation centerline (Fig. 3). The well profile was modeled with its path continuing just below the center line. However the lateral crossed the centerline at the opposing incline, then steered back below the centerline; it then continued to total depth (TD) (Fig. 4). In summary, the total length of the lateral was modeled close to the target centerline throughout the 4,500 ft production interval.
For this well, average gamma ray was the primary measurement for well placement. Resistivity, density and porosity measurements were available on three sections of this lateral. In this case, standard non-azimuthal representation of these measurements provided very little steering information or contrast across prominent gamma ray changes observed along the lateral (Fig. 5). While this finding applies to this particular reservoir, application of these non-azimuthal measurements can be effective in other reservoirs, depending on their respective rock properties. This can be evaluated from nearby vertical offset well references.
On the other hand, logging-while drilling (LWD) images for this shale play provided significant details that could be used for real-time geosteering (Fig. 6). Shallow depth-of-investigation (DOI) Photoelectric Factor (PEF) images provided near well bore details. Deep DOI gamma ray images provided images of lower resolution. However, as seen in this example, high-resolution density images detailed thin beds and clearly defined boundaries and geological features. This data can be used in real time for detailed dip determination, structural modeling and effective geosteering decision making. Target windows less than 10 ft have been effectively steered within various shale plays using Density images. Although available in real time, these images were not employed at the time of drilling this particular well but were effectively used as shown below in the post well analysis.
Completion and Production Logging
The target well was completed in nine stages, selected geometrically. They were spaced 500 ft apart with four perforation clusters per stage. The perforation cluster spacing was 110 ft with cluster length of 2 ft at 6 shots per foot (SPF). The stimulation design was similar for each stage and consisted of 12,000 bbls of slickwater with 200,000 lbs of 30/50 sand pumped at 80 bpm.
A production log was run after the completion to determine individual-stage production contribution (Fig. 7). When production log results were overlaid on the well trajectory, red lines were used to represent the percentage contribution from each perforation cluster (Fig. 8). Each square indicates a perforation cluster grouped by different colors to represent individual stages. The block across Stage 1 and 2 represents a total combined production from all perforations across the area indicated.
For shale gas development, the perception has been that as long as the lateral is placed within the target reservoir, hydraulic fracturing will provide total reservoir connectivity. This line of reasoning is challenged when the production log results indicated that not all stages contribute equally, although equivalent stimulation designs were pumped at each stage. To explain this variation, some experts hypothesized that it could be caused by lateral heterogeneity, lack of isolation during stimulation, presence of faults, presence of or lack of natural fractures and poor stimulation execution.
As interpreted by the real-time model, should the well be placed close to the centerline, this would mean that in this reservoir precise well placement adds no value and production is driven by other factors. Perhaps there is more to it than meets the eye, as detailed in the next section.
Post well assessment using Gamma Ray
Using proprietary modeling software, the real-time results were remodeled using only gamma ray data. Utilizing this single curve it was possible to make different plausible structure models yielding various results (Figs. 9 and 10). Although several possible configurations can be achieved, the two models presented here are sufficient to illustrate the problem. Both were modeled with reference to the same offset vertical well using the same horizontal data set and to tie-in their correlations to the boundary at the top of the target reservoir. The upper boundary is a field-wide marker, where an obvious increase in gamma ray occurs. It can be observed that the correlations between the measured gamma ray and modeled gamma ray overlay reasonably well for both of these models. The model in Figure 9 enters the structure as initially interpreted, crosses the center line at the syncline, and builds back up through it. The well trajectory is then maintained above the centerline, deviating further from the centerline as it approaches the toe of the lateral. Figure 10 crosses the centerline at the same depth as modeled in Figure 9. This structural model matches the well's trajectory below the centerline throughout the lateral. Including the actual real-time model, it can be concluded that although the gamma ray correlates well on three counts, all three interpretations can provide non-unique solutions to the position of the well trajectory for this structure.
To summarize, the interpretations consist of the well trajectory placed along the center line Figure 3 and 4; trajectory placement close to and above the centerline, Figure 9; and trajectory placement below the centerline, Figure 10. Ultimately, additional measurements are needed to verify which of these models represent the correct interpretation of the well trajectory in the structure.
Although LWD images were available in real time while drilling, they were only applied to the post well assessment. For this assessment, the interpreted dip is used to verify the correct structural model. Similarly performed in real time, the LWD images were imported into proprietary software to enable image interpretation and structural dip picking. The structural dips can be further evaluated in 3-dimensions for cross section visualization and structural consistency along the wellbore prior to exporting them to the geosteering software (Fig. 11). By importing the dips into the geosteering model, the interpreted structure can be validated, hence providing higher interpretation accuracy. Having a high confidence in the structural model promotes accurate steering decisions. This mitigates out-of-zone drilling that may lead to expensive and unnecessary sidetracks or poor producing intervals.
Formation dip is represented by a short green line along the well's trajectory and placed at the calculated dip angle. To validate the model, the green line should run parallel to the horizon immediately under it. Importing structural dips to the model as shown in Figures 9 and 10, it becomes obvious that the modeled structure in Figure 9 does not conform to fit with the interpreted dips, circled in black (Fig. 12). Hence, this model is clearly inaccurate. On the other hand, the model depicted in Figure 10 provides an accurate match against the interpreted dips (Fig. 13). The addition of structural dips along the lateral compares to a multipoint calibration for the structural model.
Overlaying the production logging results across Figure 13, it becomes apparent that Stage 4 is located in a lower part of the reservoir (marked as a brown sub-layer) than originally conceived (Fig. 14). Also Stage 9 that was completed in the sub-layers above the centerline showed poor production contribution, while stages close to the centerline illustrated higher and fairly consistent production. It can perhaps be derived that the sweet spot for this area is several sub-layers below the centerline, coded in green (Fig. 15). As illustrated, no data below the lateral is available to quantify sub-layers beneath it.
Additional data such as mineralogy, effective porosity, geomechanical properties, etc., and further analyses are needed to qualify production variations between the stages. Contrary to conventional belief, although Stages 4 and 9 were placed just a few feet away from good production zones, similar stimulation treatments did not seem to provide similar production results (Fig. 15). Besides obvious positional reasons within the structure, it should not be discounted that other reservoir or completion factors could play role in poor production. With accurate structural interpretation, further data acquisition would be helpful in providing answers, such as advanced LWD measurements, microseismic monitoring, etc.
Without a doubt, structural correlations used for steering in real time clearly enabled positioning of the lateral trajectory within the 190ft reservoir interval. Nevertheless, the interpretations for specific well positioning within the structure offered limited insight to the actual production potential and ensuing reservoir interpretation. Independent models using the same data set illustrated the capacity for non-unique structural model solutions created from a single gamma ray measurement. By applying additional measurements to refine the structural model, it is possible to improve the model's accuracy and remove incoherent interpretations. Incorporating additional information such as production logging data illustrates the value provided from an accurate model where in-depth understanding of production profiles can be used to redefine vertical targets for the lateral, its size and the importance of accurate well placement (Figs. 14 and 15).
While gamma ray measurement can be used effectively for vertical and high angle correlations, using this measurement alone for structural modeling may potentially provide non unique solutions in a horizontal well environment. Additional measurements such as resistivity and density images can be used for structural model verification especially in shale plays. A multipoint calibration along the lateral well trajectory for a unique solution can then be obtained for optimization across many wells. During real time drilling operations, LWD images are effectively used to steer horizontal wells for precise well placement within narrow target windows, often less than 10ft in stratigraphic thickness.
Providing an accurate account of the well's position in the structure promotes the confidence to precisely tie-in all measurements, such as LWD, wireline, hydraulic fracture monitoring and production logging to the interpreted structure. The ability to perform this task with confidence greatly offers an array of benefits, including but not limited to:
Sweet spot Geosteering, targeting lower-stress zones for efficient and cost effective hydraulic fracturing completions. These zones have been observed to deliver high drilling rates of penetration (ROP) allowing more aggressive drilling bottomhole assembly (BHA) designs that have been proven to reduce drilling time and cost.
Accurate interpretation and modeling for detailed understanding of a shale reservoir that can help improve target selection, increase initial production and ultimately field optimization.
Direct value to the interpretation of measurements for accurate reservoir characterization, optimized field planning and development.
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The authors would like to thank BP for granting the release and publication of this material. Special thanks extended to Aron Kramer for providing encouragement and valuable guidance. Finally, thanks to our entire team for their contributions and dedication.