Assimilation of drilling data produces reliable, cost-effective, engineered completions

The success of the shale business has been accomplished by lowering costs while increasing well productivity through optimized drilling and completions operations. To achieve this success, companies have leveraged technological advances and continue to search for tools to further improve performance. Advances in completions have the greatest potential to generate the next big advance, because current drilling practices create a mismatch between wellbore trajectory and geological horizons, which is inevitable when drilling long lateral sections. This results in variability along the wellbore, as its trajectory criss-crosses through the various layers of the target formation.

Fig. 1. Formation hardness guide.

Today’s completion strategies are based on the premise that lateral irregularity manifests itself as a stress profile along the horizontal. Because the fracture treatment tends to follow the path of least resistance, these variations have a direct impact on completion efficiency within each stage. Engineered completions use reservoir evaluation data to help ensure that every perforation cluster within a given stage is placed in “like rock,” reducing the impact of the lateral variability and reducing the inconsistency in treatment results and the corresponding inconsistency in production from stage-to-stage and well-to-well. This inconsistency is referred to as the “statistical nature” of shale reservoirs. Despite the industry’s success, the current completion tools and methodologies have not achieved universal acceptance. Issues include:

• High cost of acquiring reservoir evaluation data.
• The negative impact that data acquisition has on operational efficiency.
• Challenging logistics of delivering engineered completions in a high-volume arena.
• Difficulty quantifying the impact of an optimized completion on individual well production.

ENGINEERED COMPLETIONS

The industry requires an engineered solution to address these issues. In response, C&J Energy Services has introduced the LateralScience (LS) process, designed specifically to solve these problems and make engineered completions the norm on every well. LS uses existing drilling data to determine the best location for completion placement. This data includes weight-on-bit (WOB), drilling speed (RPM), torque (TOR), rate of penetration (ROP), differential pressure (ΔP), and mud flowrate (Q). A key advantage to this reservoir evaluation approach is that these data exist on most laterals, and are available at no additional cost.

To facilitate this approach, an engineer uses these drilling parameters to calculate mechanical specific energy (MSE). MSE defines the amount of work required to drill a unit volume of rock. MSE is related directly to a formation’s unconfined compressive strength (UCS), by the relationship UCS = MSE*Deff, where Deff is defined as the drilling efficiency of the rig. Within the length of a typical fracture stage, experience indicates that the drilling efficiency parameter will remain reasonably constant. This makes MSE an excellent qualitative proxy to UCS within each stage, enabling the engineered completion design process. 

Fig. 2a. Original geometric completion design (10 perforation clusters).

For ease of use, MSE is converted into a color-coded formation hardness chart, Fig. 1. While examination of the entire well is helpful, the essential work is done, one stage at a time. The workflow starts with the original geometric design, Fig. 2a. In this instance, 10 perforation clusters are spaced 24 ft apart. With the original design, perforation cluster two appears to be an issue, since it will break down early and ultimately receive a disproportionately high percentage of stimulation.

Fig. 2b. Optimized completion design minimizes the effect of lateral heterogeneity.

Figure 2b illustrates how the perforation clusters can be re-positioned to alleviate this issue, while still maintaining a reasonable spacing between clusters. The reconfigured design increases the chance of optimizing production in each stage by evenly distributing fracture treatment across at least eight of the 10 perforation clusters. This facies-based approach is more efficient than other engineered completion workflows, making the technique ideal for high-volume, multiple-well applications.

CASE STUDY 1

Fig. 3. 3,300-ft facies plots of lateral sections on LC-1H and LC-4H.

LS was used to assess if production was adversely effected by lateral heterogeneity in a Lower Cleveland well drilled in Ellis County, Okla. An offset was selected for comparison. Both were completed using a geometric design with identical schemes and treated with the same fracture program. Although both wells were completed similarly, they had significantly different production results.

Figure 3 shows the section from each well after LS facies analysis. Well LC-1H has significantly less lateral heterogeneity, with only two facies compared to offset LC-4H with four facies that change multiple times. The variability in rock strength at each perforation cluster (Fig. 4) provides valuable insight into what caused the difference in production between the two wells.

Fig. 4. Original LC-1H geometric design produced many “like rock” stages, due to low lateral heterogeneity. The LC-4H completion was less efficient, due to the well’s high lateral variability.

For the analysis, engineers surmised that the weakest facies in each stage was treated effectively. The LS analysis predicted that 63 of 80 perforation clusters in the LC-1H well were stimulated effectively. The same analysis for the LC-4H well yielded 42 effectively stimulated perforation clusters. The calculated perforation efficiency is 50% better in LC-1H, which agrees with the actual production data.

In the first year, the LC-1H well averaged 214 bopd and 571,000 cfgd, while LC-4H averaged 135 bopd and 328,000 cfgd. This equates to 59% more oil and 74% more gas. The agreement between the LS prediction and actual production data suggests that the difference in production is due primarily to the adverse effects of lateral heterogeneity during the treatment process.

CASE STUDY 2

This example uses data from three laterals drilled in the Bone Springs formation in West Texas to demonstrate LS. The subject wells are located within a one-mile radius, and were drilled in a common direction and treated in a similar manner.

Fig. 5. Comparison of actual production vs. calculated completion efficiency on three Bone Springs lateral wells.

LS analysis indicated that the section in the first well (1H) is relatively homogeneous, and that 96 of 125 geometric perforation clusters were stimulated thoroughly. The second lateral well (2H) is more heterogeneous and had an 18% lower predicted stimulation efficiency than 1H, with only 79 of the 115 perforation clusters receiving adequate stimulation. While drilling the third lateral, the formations appeared analogous to those encountered in 1H. However, the lateral drilled out of the productive zone and TD was called at the half-way point to limit financial exposure. As a result, only 40 of 85 perforated clusters were stimulated effectively, 58% less than the 1H well.

The actual production totals for the first year are in good agreement with the LS predictions, Fig. 5. The 1H well produced 161,355 bbl of oil, while the 2H well produced 112,478 bbl, 30% less than the 1H well. The 3H well produced 68,801 bbl of oil in its first year, which is 57% less than 1H. The LS analysis successfully ranked the wells by productivity and did a good job quantifying the production differences between the three wells.

CASE STUDY 3

Case study 3 uses data from a West Texas well drilled in the Delaware basin, May 2014. The well has a 4,700 ft lateral, which was drilled through the Wolfcamp shale to a MTD of 14,600 ft. Operators working in the Wolfcamp play frequently use reservoir evaluations to engineer completions, because the reservoir is highly heterogeneous. Unlike most horizontal shale operations, this well was drilled with WBM to enable the operator to run an image log in the lateral. The evaluation included a comprehensive suite of open-hole logs. The goal was to quantify heterogeneity along the lateral and identify intervals with open, natural fractures. These naturally occurring formation openings would be the target of the completion design for this well.

Fig. 6. Comparison of open-hole logs to drilling-based facies log along a 50-ft section.

The log data are presented alongside the drilling-based facies logs in Fig. 6. The GR log is shown in the depth track at the bottom of the figure, and it suggests a mostly homogeneous section. Directly above the depth are curves that show the results of an image interpretation that quantify the presence of drilling-induced fractures (shaded green), healed natural fractures (light blue), and open natural fractures (royal blue). Just above that are the static and dynamic images from the FMI log. The interpretation shows that the majority of the open natural fractures (royal blue) reside only in the middle of this particular interval.

Fig. 7. LS-completed wells (circled) produce good results compared to offset wells.

The color-coded LS logs are shown directly above the open-hole logs. The facies logs suggest that the middle of this section (red) is different from the facies at either end (yellow and orange). The middle section, where the FMI interpretation identifies open natural fractures, was the most difficult to drill and exhibited the highest MSE. This demonstrates that by focusing on the red facies in this lateral, the operator could have successfully identified that the middle of the 50-ft section would be the best location for their perforation clusters. The agreement between image logs and the results of the LS analysis for this section is typical of the rest of the well, and demonstrates that MSE is a reliable indicator of “like rock” in the Wolfcamp.

CASE STUDY 4

This case study focuses on five West Texas Wolfcamp laterals drilled by one specific operator in 2015, and completed using the LS methodology. To ensure a comprehensive evaluation, the five wells are compared to 161 Wolfcamp horizontals drilled and completed in 2014-2015. Figure 7 is a cross-plot that illustrates the relationship between total proppant pumped during completion and the three-month cumulative production for all 166 wells. The data points for each of the five subject wells completed based on an LS analyses are circled.

The dotted line depicts average well production. All five of the LS subject wells are better than the average. Two demonstrate superior performance compared to offset wells that received comparable completions.

RELIABLE METHODOLOGY

The LS procedure has been deployed successfully and removed the four main obstacles to engineered completions:

• Eliminates the high cost of data acquisition by leveraging commonly available data.
• Avoids the negative impact on operational efficiency associated with data acquisition required for other reservoir evaluation techniques.
• Provides an efficient workflow, which can be deployed in a high-volume shale environment.
• Enables operators to effectively analyze historical completions, to quantify the difference between geometric and engineered completions.