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Achieving high hydrocarbon recovery is challenging in unconventional tight and shale reservoirs. Although EOR/EGR processes could potentially improve the recovery factor beyond the primary depletion, large-scale field application of these processes are not yet established in these reservoirs. This session will focus on the latest research trends, modelling and experimental work to better understand issues involved in improved economic recovery from such reservoirs.
Ibrahim, Ahmed Farid (Shear Frac Group LLC) | Ibrahim, Mazher (Shear Frac Group LLC) | Sinkey, Matt (Shear Frac Group LLC) | Johnston, Thomas (Shear Frac Group LLC) | Johnson, Wes (Shear Frac Group LLC)
The most common stimulation technique for shale production is multistage hydraulic fracturing. Estimating fracture geometry is a focal parameter to judge the fracture operation and predict the well performance. Different direct and indirect techniques can be used for fracture diagnostics to estimates fracture geometries. The current study combines fracture measurements and pressure transient analysis to estimate fracture surface area on each stage and to estimate production as a pseudo production log.
The numbers and kinds of fractures were calculated as a function of treating pressures, injection rates, proppant concentrations, and formation properties to compute fracture surface area (FSA). Pressure transient analyses were then conducted with the leak-off data upon completion of each frac stage to estimate the producing surface (PSA). The fall-off data was processed first to remove the noise and water hammering effects. The PTA diagnostic plots were used to define the flow regime and the data were matched with an analytical model to calculate producing surface area.
Tensile and shear fractures are both created during the injection of frac fluids. Shear fractures are caused by movement in already existing natural (fluid expulsion) fractures found in all shale source rocks. Shear fractures form a pressure below the minimum horizontal stress. These shear fractures take advantage of the rock fabric and develop higher surface area than tensile fractures for the same given volumes of water and sand.
FSA is a measure of permeability enhanced area due to hydraulic fracturing. Producing surface area is the resulting effective flow areaconnected to the wellbore. Diagnostic plots showed a linear and radial flow regime depending on the formation and the completion design. Good correlations were found between PSA and FSA results. In general, higher FSA produces higher PSA. In cases where producing surface area was higher than expected from fracture surface area, communication was found with offset wells. When FSA higher than PSA were found, it was usually caused by increased stress from too close offset wells.
Combining FSA and PSA measurements provides forecasts of production for each stage and helps to optimize well spacing at the end of each frac stage.
Schlecht, Mathias (Biota Technology) | Sawadogo, Jordan (Biota Technology) | Sadeghi, Simin (Biota Technology) | Reeve, Nico (Biota Technology) | Haggerty, Matthew (Biota Technology) | Liu, Joanne (Biota Technology) | Ursell, Luke (Biota Technology)
Permian operators have dramatically increased the number of multi-stage fractured horizontal wells over the past 5 years and face challenges associated with maximizing production of existing wells while developing new acreage and benches, all the while meeting capital return requirements. Over that time, DNA diagnostics have been applied successfully to more than 1000 wells throughout the Permian Basin to help operators reduce uncertainties ranging from drained rock volume, well-well communication, and sources of water production.
When subsurface conditions change, microbes change, and the DNA from microbes can be used to profile total fluid flow (water + oil phases) from benches and between wells. It therefore serves as a powerful tool to provide a range of answers, using advanced analytics and integration with various data sets. In this study, we will provide the background of DNA diagnostics and related analytics, along with the latest insights into viable operating environments. We also highlight recent Permian basin projects that have used DNA in conjunction with operator data to reduce uncertainty about subsurface conditions.
We will show Total Fluid Logs, which are based on comparing DNA signatures from produced fluids with a DNA stratigraphy log. Total Fluid Logs are utilized to 1) constrain interpreted fracture heights, and 2) work in combination with pressure and production data for Rate Transient Analysis (RTA) for significantly improved estimation of the half-length. The case histories will illustrate the differences between production rates and confirmed fracture height and half-length, and a discussion of microseismic is included.
We show how produced fluid collection during pad completions can elucidate well-well communication and demonstrate the impact of completion size and completion order on effective drainage heights. DNA changes in produced fluids can be compared to production data to reveal the timing and impact of frac hits between wells during zipper completions.
Finally, we provide a suggested workflow for analyzing water contributions out of target in the diagnosis of problem wells. Petrophysical logs can be compared to drainage height assessments to help reveal from which depths water may be producing and can be integrated with production data for a more complete subsurface understanding.
DNA diagnostics represent a complementary, cost effective, minimum environmental footprint and low risk tool for operators to easily integrate into existing production and engineering workflows for monitoring well health and subsurface conditions across time.
The pressure decline data after the end of a hydraulic fracture stage is sometimes monitored for an extended period of time (30 minutes to hours). However, this data is not analyzed and often ignored or underappreciated due to a lack of suitable models for closure of propped fractures. In this study, we present a new approach to model and analyze pressure decline data that is available at the end of each plug and perf stage in horizontal wells. The new model, interpretation method and specialized plots presented in this study allow us to quantify closure stress, average pore pressure inside the stimulated reservoir volume (SRV) and normalized fracture stiffness/compliance evolution along the entire horizontal wellbore without additional data acquisition costs. Analysis of field stage-by-stage pressure decline data shows that the interpreted results are consistent with the analysis of DFIT data from an offset well for the same formation. We found that the early-time stage-by-stage pressure decline trend is controlled by progressive hydraulic fracture closure on the proppant pack, while late-time pressure decline reflects linear flow. Thus, the pressure decline rate alone is not a reliable indicator of the productivity or stimulation efficiency of a certain stage. When DFIT data is not available, pressure decline analysis of a main hydraulic fracturing stage can be used even if it can be monitored for a relatively short period of time (1 hour). Most important of all, we show that the slope of pressure derivative on a log-log plot and the normalized fracture stiffness plot can be used to infer the uniformity of proppant distribution.
Sui, Weibo (China University of Petroleum, Beijing) | Zhang, Di (China University of Petroleum, Beijing) | Cheng, Si (China University of Petroleum, Beijing) | Zou, Qilin (Beijing Perception Technology Co.) | Fu, Xiaosong (Beijing Perception Technology Co.) | Ma, Zehao (Beijing Perception Technology Co.)
With the gradually increasing applications of fiber-optic distributed temperature sensors (DTS) in unconventional resources exploitation, academic researchers have developed general theoretical models for forward temperature simulation and inverse flow rate profiling during and after stimulation workover. However, there have been no enough field applications for the established theoretical models and some practical issues still exist such as different completion scenarios are still lack of consideration in current models. This paper presents a DTS flow profiling case for a horizontal multi-stage fractured well in tight gas reservoirs with open-hole packer completion scenarios by applying a newly improved theoretical model.
In this paper, for the modeling part, we start with the semi-analytical wellbore-fracture-reservoir coupled flow/thermal model but improve it to consider open-hole packer completion scenario. Compared with the conventional cased, cemented and perforated completion style, the fracture initiation points in open-hole stimulated well are more effected by near wellbore in-situ stress field. Therefore, the open-hole packer completion possibly forms a two-fold flow regime. The formation fluid firstly flows through the fracture into the open-hole annular space between formation and the packer liner, then flow along the annular space until meet the frac port on the production pipe. The two-fold flow regime results in double temperature drops due to Joule-Thompson cooling effect. The original theoretical model is improved by adding a simulation sub-region representing open-hole annular which helps to understand the flow and heat transfer inside it.
With the improved mathematical model, DTS monitoring data during a three-rate production test in a horizontal multi-stage fractured well in Erdos Basin of China was simulated and analyzed. The improved model with open-hole packer completion was applied and then the gas flow profiling was accomplished.
This paper presents a case study of fracture interaction mitigation in a multistage horizontal stimulation of an offshore Black Sea well. A multi-faceted approach in applying lessons learned and pre-job geo-mechanical analysis of depletion-induced stress differential and its effects on fracture interactions will be discussed. Details of on-the-job, real-time bottom-hole pressure monitoring of nearby wells, with the effort of on-the-fly pumping schedule changes, will also be provided.
An analysis was conducted on past fracture interactions observed from multistage stimulation jobs in the area. Depletion, distances between producing wells, and a stress analysis was performed using fracture simulation software, and a consequent analysis of fracture geometry was applied. A bottom-hole gauge pressure profile assessment of nearby wells, including the pre-stimulation, shut-in, and post-stimulation period of the targeted well, was completed. A redesigned treatment was applied, considering a mitigation plan for potential on-the-fly changes during pumping. A holistic tracer analysis of production contribution between stages and wells was performed, with the goal of understanding possible crossflow of production fluids.
Past-fracture interaction events have been analyzed, and clear drivers for fracture hit communication were observed. Extreme depletion effects were a primary factor in enabling fracture communication. The preferential fracture growth was further enabled owing to the continuous production of nearby wells and no shut-in implementation. The 3D geo-mechanical model was built using pertinent data from the targeted and nearby wells. The model was further optimized using fracture geometry outputs, and constraints were input to limit the fracture growth and avoid communication. The outcome of the analysis showed a clear driving force behind the interactions was depletion. An on-the-job assessment of diagnostic tests yielded a heterogeneous behavior of the horizontal segment, further proving stress differentials along the lateral. An overall chemical tracer analysis of the targeted and nearby wells was completed using pre- and post-stimulation fluid samples. The results were crucial in understanding the stimulation approach and possible crossflow effects due to fracture communication. Additionally, using bottom-hole temperature readings, a rudimentary cool-down and heat-back analysis was performed to better understand possible fluid interactions with nearby wells and optimize fluid design.
Intra-stage fracture interference presents unique events and challenges that are typically managed on a case-by-case basis, and this work presents the critical analyses that are paramount to planning stimulation treatments in highly depleted segments and reservoirs with wells in close proximity.
Distributed temperature sensing (DTS) is a valuable tool to diagnose multistage hydraulic fracture treatments. When a stage interval is shut-in, the clusters which take more fluid during pumping warm up more slowly. Therefore, the fluid volume injected into each cluster can be quantitatively interpreted by numerical inversion of the warm-back temperature behavior. This general concept assumes that the different warm-back behavior is controlled by only the injected fluid volume, however, recent observations of DTS data indicate that completion configurations significantly influence the warm-back behavior.
This paper investigates the completion effects on the DTS interpretation. In ideal conditions, when a stage is fractured, the upstream stage intervals should show an almost uniform temperature that is close to the injected fluid temperature. This is due to the high fluid velocity of injected fluid in the wellbore, and the upstream intervals have not been perforated (non-communicating intervals). Thus, the only heat transfer is heat conduction between the wellbore fluid and the surrounding reservoir. But the field DTS data show considerably irregular variations in temperature along the upstream stage intervals. These variations are caused by the completion effects. The non-uniform temperature profile is caused by different heat transfer behavior induced by completion hardware along the production casing string such as joints, clamps, and blast protectors, and by the sensing cable location in the cement, as well as the cement quality. Since the heat transfer behavior impacts the warm-back behavior as well as the temperature profile, the completion effects need to be considered in DTS interpretation.
A method of DTS interpretation considering the completion effects to diagnose multistage fracture treatments was developed. Since the heat transfer between a wellbore and a reservoir depends on the overall heat transfer coefficient describing heat conduction through the completion in a forward model, this parameter needs to be tuned along the entire wellbore. To calibrate the completion effect, the temperature inversion is conducted using the temperature measured at a stage interval that is upstream of a stage interval currently being treated. Since the interpreted stage interval is not perforated at that time, the thermal behavior at the non-communicating interval is governed by only the heat conduction through the completion environment. Once the effective values of the overall heat transfer coefficient are estimated along the interpreted stage interval, they can be assumed to be constant physical parameters. Then, the fluid volume distribution is interpreted by using the effective overall heat transfer coefficient profile along each interval.
The interpretation method developed in this study was demonstrated using field data, and it was concluded that the new DTS interpretation method provides more accurate diagnosis of fracture treatments.
One of the challenges of unconventional resource development is the identifying and preventing casing failures caused by the hydraulic fracturing process. Multiple mechanisms may be responsible for casing deformation and/or failures, starting with the rock properties of the formation, the wellbore configuration, quality control of tubulars, and operational aspects during drilling and completion. This paper presents two case studies where casing issues were discovered during the drill out of frac plugs following multi-stage fracturing treatments. The objectives of these studies are (a) to determine the cause and nature of the casing failures, (b) to recommend changes to future completion programs to prevent similar operational issues, and (c) to develop a model that automatically identifies these failures.
The subject wells are located in two very different basins: the Eagle Ford trend in the Brazos Valley (BV) area of south Texas and the Powder River Basin (PRB) in Wyoming. In both studies, the casing issues could be directly correlated to Abnormal Pressure Behaviors (APBs) observed during fracturing. A total of 486 stages, completed in 12 different wells, were reviewed using a cloud-based application that allows stages to be examined individually, or as groups. Since then, five additional wells have been added to the data set. After problem stages were identified, the completion team worked with the drilling engineers and geologists to determine the mechanisms causing the casing damage.
Tight spots encountered during frac plug drill out in the BV wells directly correlated with stages completed in geological transition zones between the Eagle Ford and Woodbine formations. Once this was recognized, the team implemented operational contingencies to fracture designs for stages completed in BV transition zones. In the PRB wells, after reevaluating the post-mill inspection of the casing, the damage was found to be poor casing quality control. The location of casing deformations and/or failures directly correlated with stages that displayed evidence of frac plug failure. Moving forward, the PRB completion supervisors were made aware of potential issues, and alternative procedures were developed for both fracturing and drill out operations that utilized the questionable casing. As of this time, no additional casing issues have occurred.
In these studies, identification of the problem stages was initially performed manually (stage-by-stage) using a cloud-based analytics platform (CBAP). During the process, it was recognized that the two types of problem stages had their own characteristic pressure signature. A machine learning algorithm was developed that automatically identifies plug failure, which is indicated by a sudden unexplained pressure drop in the absence of rate changes. Transition stages could be easily identified through the use of stage variance plots (e.g., comparing maximum/average rates and pressures across multiple stages and wells) and also through machine learning algorithms that identified unexpected pressure increases followed by sharp pressure drops.