In effort to increase well performance via stimulation efficiency, an active Permian operator (“Operator”) selected Quantico Energy Solutions (“QES”) to provide horizontal well log data for an upcoming completion project in the Wolfcamp formation in Reeves County, Texas in the Permian basin. The Operator’s objective was to optimize the effective stimulation reservoir volume by rapidly reaching the designed injection rate during the hydraulic stimulation treatment for each stage. Early pumping time opening of the perforation clusters over 6,500 ft. of lateral wellbore (subdivided into 43 stages) maximizes the fracture geometry. The challenge was to provide an economic completion solution of optimal perforation cluster locations for each stage with minimal risk and cost. Conventional open-hole logging solutions would have incurred a minimum cost of $200,000. Mud Logging and cuttings data provide a geological/lithological description of the wellbore, but do not address the rock mechanics issues. The performance of the QES engineered perforation cluster depth locations were evaluated using pressure pumping and post frac modeling by StrataGen who was contracted by the Operator.
QES provided an array of geomechanical logs (“QFrac”) that were derived from the well’s drilling data and enabled a 80% savings to the Operator when compared to the cost of conventional e-line small diameter tool string pump down logging operations. The QFrac logs were generated within a few days of receiving the existing drilling data from the Operator, which allowed sufficient time for the pressure pumping service company and the stimulation consulting company to optimize the treatment designs based on geo-mechanical properties along the 6,500 ft. length of the lateral section. The necessary datasets were integrated as standard well log presentations and associated digital data files. QES’ solution did not require any tools to be placed into the wellbore thus reducing lost-in-hole and operational risk for the Operator.
QFrac provided an array of geomechanical properties, presented in a well log format upon which decisions to optimize perforation cluster placement were based. The stage locations remained geometric consistent with prior practices. These optimized perforation cluster placements took into consideration the key parameters that affect the quality of the hydraulic stimulation treatment such as horizontal stress (Sh), porosity (EPOR/DPOR), brittleness, and permeability index. Having QFrac during the planning phase enabled the Operator to effectively evaluate various treatment parameters such as (a) placing perforation clusters into rock with similar stress; (b) optimization of the injection rate based on Sh and Young’s moduli; and (c) selecting the appropriate fluid systems based on anticipated leak-off and proppant concentration.
The 43 planned stages were executed as designed with no major operational issues of note. The pressure pumping data was analyzed for each stage (see Figure 1) and concluded that relative to the predicted stress, the recorded stress exhibited average variance on the optimized stages of only 8% (maximum variance was 15% on non-optimized stages). Two-thirds of the stages exhibited variance less than 10%.
QES provided the Operator with critical formation data for a horizontal well that would have been cost prohibitive to log using conventional methods. The Operator was able to achieve designed injection rate faster due to the perforation placement optimization of having minimal stress variations between each perforation cluster for the stage (see Figure 2). This resulted in having more perforations broken down and open per stage.
Additionally, in many reservoirs rapidly achieving design injection rate is an important and many times difficult task toward a quality treatment in addition to breaking down perf sets adequately and this can add unnecessary cost and time to the completion project. By optimizing perforation placement with QES data and understanding rock properties within each stage, the Operator had higher confidence that all clusters were broken down uniformly and therefore capable of accepting frac fluid and being optimally stimulated. The rapid time to desired injection rate, due to effective perforation opening, saved the Operator approximately 11% on frac costs by reducing the large amounts of costly time and additional fluid injected attempting to breakdown perf sets to achieve design injection rate.
The following pressure pumping data (Figure 3) is from an offset well completed in the same stratigraphic interval and the geometric/uniform perforation cluster spacing was utilized. The white curve top graph is the surface treating pressure, and the yellow curve is the injection rate. The maximum surface pressure obtained, but the desired injection rate of 70+ BPM was not obtained until approximately 112 minutes of pumping time. This response is indicative of poor perforation opening mechanisms, which can result in poor fracture geometry for the stage.
If we focus on one stage of the optimized perforation cluster selection process (Figure 4), the flow rate (yellow curve top graph) was increased from 15 bpm to approximately 78 bpm at 51-52 minutes and the corresponding bottom hole pressure (white curve lower graph) rapidly and uniformly increased. Had there been perforation or near well bore friction issues there would have been spikes on both the rate and bottom hole pressure data. The near uniformity of the bottom hole pressure while the proppant concentration (red curve bottom graph) was increased to 4.0 ppg, illustrates effective in-zone fracture propagation. The surface pressure anomaly (white curve top graph) occuring between 100 and 106 minutes is related to blender/friction reducer additive issues.
Using QES data, the Operator observed reduced time to achieve design rate (Figure 5). By achieving a higher perforation efficiency, the ability to breakdown a higher amount of perf sets sooner was demonstrated. The resulting higher efficiency shown here with a regional average case of thirty-seven minutes (37) of net pumping time (net pumping time extracts time spent injecting acid at planned lower rates) to breakdown all perf sets effectively and achieve design rate. The average time to achieve rate using QES data was twenty-three (23) minutes. From the toe to the heel of the well, the decrease in time for each successive stage resembled the linear trend one would expect due to pipe friction.
The resulting economic impact was estimated at approximately $230,000 (11%) savings on frac costs and reducing overall pumping time by over ten (10) hours. The resulting perforation clusters were more effectively treated which should also positively impact production by converting understimulated/non-stimulated perf clusters to contributing/higher contributing perf clusters by optimizing perforation placement.
Total cost reduction delivered to Operator:
A) $230k saved on time and fluids due to faster time to achieve designed injection rate
B) $200k saved from avoiding a conventional open hole logging run