Real-time Cluster Efficiency
Distributed fiber optic sensing for uniform fracture stimulation
Evaluating stimulation performance and well spacing early in development can increase a projects Net Present Value. This is especially true when developing stacked intervals. For example, studies have shown that plug-and-perf completions often produce under-performing perforation clusters and undesired inter-well communication.
To address under-performing perforation clusters, operators are combining Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS)measurements to calculate the amount of fluid and proppant placed in each cluster on the fly to enable optimized decision-making throughout a project ensure more effective fracturing on current and future wells.
Recent observations from fiber optic DAS and DTS indicate that a majority of treatment volume is limited to only one or two dominate clusters near the heel-side of a treatment stage—leaving the remaining stage clusters under stimulated.
With a large majority of perforation clusters failing to contribute to production, you can’t help but ask: Where’s my proppant going and why?
Assessing cluster efficiency, fluid distribution and diverter effectiveness
There are many possible reasons for uneven reservoir stimulation, such as stress shadowing interference between fractures, local heterogeneity, the effectiveness of zonal isolation between stages, stimulation design (pumping schedules and fluid/proppant selection), and the variation in natural fracture systems surrounding the well.
Fiber optic monitoring, such as DAS and DTS can be used to assess cluster efficiency, fluid and sand distribution and diverter effectiveness.
On a recent spacing pilot in the Anadarko Basin, home to several stacked interval reservoirs, a five-well project equipped with OptaSense Distributed Fiber Optic Sensing offered another explanation for the heel-side bias. For this project fiber optic derived DAS and DTS measurements provided the operator an opportunity to monitor fluid movement during fracture stimulation and warm-back before the well was produced.
During treatment, acoustic and temperature data confirmed inadvertent diversion away from toe-side clusters, and acceleration of the already-dominant heel-side clusters. Using algorithms applied to DAS data, proppant volumes per cluster were calculated revealing highly uneven proppant distribution among multiple clusters even when fluid is uniformly distributed
The DAS measurements captured in this pilot project suggested a strong heel bias was present in a majority of stages. The uneven distribution, caused by interference between adjacent fractures within a given stage and from preceding fracture stages, resulted in a consistent geometric predominance for fracture growth toward the most heel-ward perforation cluster.
A variety of completion variables, such as perforation designs, fluid systems, diverter and proppant size, were tested to identify the optimal treatment for improved fluid distribution.
Using these measurements, the operator used calculated proppant placement to monitor diversion efficiency in real-time during the fracture and took action to modify the treatment, which resulted in more even fluid and treatment distribution. After modeling the improved distributions derived from fiber optic monitoring, a new well completion and stimulation design resulted in more equal fracture heights and half lengths, as well as increased the overall effective fractures in the wellbore.
Multiple optimizations in pressure pumping strategy were discovered during the variable testing using real-time DAS and DTS. The pumping schedule was altered to test different rations of slick water and high viscous fluids, ratios in proppant sizes and concentrations of proppant within the various fluids.
Using DAS and DTS to estimate fluid and proppant placement enabled the operator to identify the root problem and implement an effective proppant and fluid treatment (aligned with an optimized pressure pump schedule) that mitigated the uneven stimulation. The result, improved cluster efficiency and more uniform proppant/fluid distribution on current and future stages and wells.