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Abstract Distributed Fiber Optics (DFO) technology has been the new face for unconventional well diagnostics. This technology focuses on measuring Distributed Acoustic Sensing (DAS) and Distrusted Temperature Sensing (DTS) to give an in-depth understanding of well productivity pre and post stimulation. Many different completion design strategies, both on surface and downhole, are used to obtain the best fracture network outcome; however, with complex geological features, different fracture designs, and fracture driven interactions (FDIs) effecting nearby wells, it is difficult to grasp a full understanding on completion design performance for each well. Validating completion designs and improving on the learnings found in each data set should be the foundation in developing each field. Capturing a data set with strong evidence of what works and what doesn't, can help the operator make better engineering decisions to make more efficient wells as well as help gauge the spacing between each well. The focus of this paper will be on a few case studies in the Bakken which vividly show how infill wells greatly interfered with production output. A DFO deployed with a 0.6" OD, 23,000-foot-long carbon fiber rod to acquire DAS and DTS for post frac flow, completion, and interference evaluation. This paper will dive into the DFO measurements taken post frac to further explain what effects are seen on completion designs caused by interferences with infill wells; the learnings taken from the DFO post frac were applied to further escalate the understanding and awareness of how infill wells will preform on future pad sites. A showcase of three separate data sets from the Bakken will identify how effective DFO technology can be in evaluating and making informed decisions on future frac completions. In this paper we will also show and discuss how DFO can measure real time FDI events and what measures can be taken to lessen the impact on negative interference caused by infill wells.
Brinkley, Kourtney (Devon Energy) | Ingle, Trevor (Devon Energy) | Haffener, Jackson (Devon Energy) | Chapman, Philip (Devon Energy) | Baker, Scott (Devon Energy) | Hart, Eric (Devon Energy) | Haustveit, Kyle (Devon Energy) | Roberts, Jon (Devon Energy)
Abstract This case study details the use of Sealed Wellbore Pressure Monitoring (SWPM) to improve the characterization of fracture geometry and propagation during stimulation of inter-connected stacked pay in the South Texas Eagle Ford Shale. The SWPM workflow utilizes surface pressure gauges to detect hydraulically induced fracture arrivals athorizontal monitor locations adjacent to the stimulated wellbore (Haustveit et al. 2020). A stacked and staggered development in Dewitt County provided the opportunity to jointly evaluateprimary completion and recompletion efforts spanning three reservoir target intervals. Fivemonitor wells at varying distances across the unit were employed for SWPM during the stimulation of four wells. An operational overview, analysis of techniques, correlation with seismic attributes, image log interpretations, and fracture model calibration are provided. Outputs from this workflow allow for a refined analysis ofthe overall completion strategy. The high-density, five well monitor array recorded a total of 160 fracture arrivals at varying vertical and lateral distances, with far-field fracture arrivalsprovidingsignificant insight into propagation rates and geometry. Apronounced trend occurred in both arrival frequency and volumes pumped as monitor locations increased in distance from the treatment well. Specific to target zone isolation, it was identified that traversing vertically in section through a high stress interval yielded a 30% reduction inarrival frequency. An indirect relationship between horizontal distance and arrival frequency was also observed when monitoring from the same interval. A decrease in fracture arrivals from 70% down to 8% was realized as offset distance increased from 120 to 1,700 ft. The results from this study have proven to be instrumental in guiding interdisciplinary discussion. Assessing fracture geometry and propagation during stimulation, particularly in the co-development of a stacked pay reservoir, is paramount to the determination of proper completion volume, perforation design, and well spacing. Leveraging the observations of SWPM ultimately provides greater confidence in field development strategy and economic optimization.
Abstract With a recent trend in increased infill well development in the Midland basin and other unconventional plays, it has been shown that depletion has a significant impact on hydraulic fracture propagation. This is largely because production drawdown causes in-situ stress changes, resulting in asymmetric fracture growth toward the depleted regions. In turn, this can have a negative impact on production capacity. For the initial part of this study, an infill child well was drilled and completed adjacent to a parent well that had been producing for two years. Due to drilling difficulties, the child well was steered to a new target zone located 125 feet above the original target. However, relative to the original target, treatment data from the new zone indicated abnormal treatment responses leading to a study to evaluate the source of these variations and subsequent mitigation. The initial study was conducted using a pore pressure estimation derived from drill bit geomechanics data to investigate depletion effects on the infill child well. The pore pressure results were compared to the child well treatment responses and bottom hole pressure measurements in the parent well. Following the initial study, additional hydraulic fracture modeling studies were conducted on a separate pad to investigate depletion around the infill wells, determine optimal well spacing for future wells given the level of depletion, and optimize treatment designs for future wells in similar depletion scenarios. A depletion model workflow was implemented based on integrating hydraulic fracture modeling and reservoir analytics for future infill pad development. The geomechanical properties were calibrated by DFIT results and pressure matching of the parent well treatments for the in-situ virgin conditions. Parent well fracture geometries were used in an RTA for an analytical approach of estimating drainage area of the parent wells. These were then applied to a depletion profile in the hydraulic fracture model for well spacing analysis and treatment design sensitivities. Results of the initial study indicated that stages in the new, higher interval had higher breakdown pressures than the lower interval. Additionally, the child well drilled in the lower interval had normal breakdown pressures in line with the parent well treatments. This suggests that treatment differences in the wells were ultimately due to depletion of the offset parent well. Based on the modeling efforts, optimal infill well spacing was determined based on the on-production time of the parent wells. The optimal treatment designs were also determined under the same conditions to minimize offset frac hits and unnecessary completion costs. This case study presents the use of a multi-disciplinary approach for well spacing and treatment optimization. The integration of a novel method of estimating pore pressure and depletion modeling workflows were used in an inventive way to understand depletion effects on future development.
Abstract A seven-step workflow to help subsurface teams establish an initial thesis for optimal completion design (cluster spacing, proppant per cluster) and well spacing in emerging / under-explored resource plays is proposed and executed for the Powder River Basin Niobrara unconventional oil play. The workflow uses Rate Transient Analysis (RTA) to determine the parameter and then walks the reader through how to sequentially decouple the parameter into its constituent parts (frac height (h), number of symmetrical fractures achieved (nf), permeability (k) and fracture half-length (xf)). Once these terms were quantified for each of the case study wells, they were used in a black oil reservoir simulator to compare predicted verses actual cumulative oil performance at 30, 60, 90,120 & 180 days. A long-term production match was achieved using xf as the lone history match parameter. xf verses proppant per effective half-cluster yielded an R value of > 0.90. 28 simulation scenarios were executed to represent a range of cluster spacing, proppant per cluster and well spacing scenarios. Economics (ROR and/or NPV10/Net Acre) were determined for each of these scenarios under three different commodity pricing assumptions ($40/$2.50, $50/$2.50 and $60/$2.50). An initial thesis for optimal cluster spacing, proppant per designed cluster and well spacing were determined to be 12’, 47,500 lbs and 8-14 wells per section (based on whether or not fracture asymmetry is considered) when WTI and Henry Hub are assumed to be $50 & $2.50 flat.
Ajisafe, Foluke (Schlumberger) | Reid, Mark (Lime Rock Resources) | Porter, Hank (Lime Rock Resources) | George, Lydia (Former employee of Schlumberger) | Wu, Rhonna (Former employee of Schlumberger) | Yudina, Kira (Former employee of Schlumberger) | Pena, Alejandro (Schlumberger) | Ejofodomi, Efe (Schlumberger) | Artola, Pedro (Schlumberger)
Abstract Increased drilling of infill wells in the Bakken has led to growing concern over the effects of frac or fracture hits between parent and infill wells. Fracture hits can cause decreased production in a parent well, as well as other negative effects such as wellbore sanding, casing damage, and reduced production performance from the infill well. An operator had an objective to maximize production of infill wells and decrease the frequency and severity of frac hits to parent wells. The goal was to maintain production of the parent wells and avoid sanding, which had the potential to cause cleanouts. Infill well completion technologies were successfully implemented on multiwell pads in Mountrail County, Williston basin, to minimize parent-child well interference or negative frac hits on parent wells for optimized production. Four infill (child) wells were landed in the Three Forks formation directly below a group of six parent wells landed in the Middle Bakken. The infill well completion technologies used in this project to mitigate frac hits included far-field diverter, near-wellbore diverter, and real-time pressure monitoring. The far-field diverter design includes a blend of multimodal particles to bridge the fracture tip, preventing excessive fracture length and height growth. The near-wellbore diverter consists of a proprietary blend of degradable particles with a tetra modal size distribution and fibers used to achieve sequential stimulation of perforated clusters to maximize wellbore coverage. Hydraulic fracture modeling with a unique advanced particle transport model was used to predict the impact of the far-field diverter design on fracture geometry. Real-time pressure monitoring allowed acquisition of parent well pressure data to identify pressure communication or lack of communication and implement mitigation and contingency procedures as necessary. Real-time pressure monitoring was also used to optimize and validate the far-field diversion design during the job execution. The parent well monitored was 800 ft away from the closest infill well and at high risk for frac hits due to both the proximity to the infill well and depletion. In the early stages of the infill well stimulation, an increase in pressure up to 600 psi was observed in the parent well. The far-field diverter design was modified to combat the observed frac hit, after which a noticeable drop in both frequency and magnitude of frac hits was observed on the parent well. This is the first time the far-field diverter design optimization process was done in real time. After the infill wells stimulation treatment, production results showed a positive uplift in oil production for all parent wells at an average of 118%. Also, only two out of seven parent wells required a full cleanout, resulting in savings in well cleanup costs. Infill well production data was compared with the closest parent well landed in the same formation (Three Forks). At about a year, the best infill well production was only 10% less than the parent well with similar completion design and the average infill well production approximately 18% less than the parent well. Considering the depletion surrounding the infill wells, production performance exceeded expectations.
Abstract The analyses of parent-child well performance is a complex problem depending on the interplay between timing, completion design, formation properties, direct frac-hits and well spacing. Assessing the impact of well spacing on parent or child well performance is therefore challenging. A naïve approach that is purely observational does not control for completion design or formation properties and can compromise well spacing decisions and economics and perhaps, lead to non-intuitive results. By using concepts from causal inference in randomized clinical trials, we quantify the impact of well spacing decisions on parent and child well performance. The fundamental concept behind causal inference is that causality facilitates prediction; but being able to predict does not imply causality because of association between the variables. In this study, we work with a large dataset of over 3000 wells in a large oil-bearing province in Texas. The dataset includes several covariates such as completion design (proppant/fluid volumes, frac-stages, lateral length, cluster spacing, clusters/stage and others) and formation properties (mechanical and petrophysical properties) as well as downhole location. We evaluate the impact of well spacing on 6-month and 1-year cumulative oil in four groups associated with different ranges of parent-child spacing. By assessing the statistical balance between the covariates for both parent and child well groups (controlling for completion and formation properties), we estimate the causal impact of well spacing on parent and child well performance. We compare our analyses with the routine naïve approach that gives non-intuitive results. In each of the four groups associated with different ranges of parent-child well spacing, the causal workflow quantifies the production loss associated with the parent and child well. This degradation in performance is seen to decrease with increasing well spacing and we provide an optimal well spacing value for this specific multi-bench unconventional play that has been validated in the field. The naïve analyses based on simply assessing association or correlation, on the contrary, shows increasing child well degradation for increasing well spacing, which is simply not supported by the data. The routinely applied correlative analyses between the outcome (cumulative oil) and predictors (well spacing) fails simply because it does not control for variations in completion design over the years, nor does it account for variations in the formation properties. To our knowledge, there is no other paper in petroleum engineering literature that speaks of causal inference. This is a fundamental precept in medicine to assess drug efficacy by controlling for age, sex, habits and other covariates. The same workflow can easily be generalized to assess well spacing decisions and parent-child well performance across multi-generational completion designs and spatially variant formation properties.
Abstract Well spacing and stimulation design are amongst the highest impact design variables which can dictate the economics of an unconventional development. The objective of this paper is to showcase a numerical simulation workflow, with emphasis on the hydraulic fracture simulation methodology, which optimizes well spacing and completion design simultaneously. The workflow is deployed using Cloud Computing functionality, a step-change over past simulation methods. Workflow showcased in this paper covers the whole cycle of 1) petrophysical and geomechanical modeling, 2) hydraulic fracture simulations and 3) reservoir simulation modeling, followed by 4) design optimization using advanced non-linear methods. The focus of this paper is to discuss the hydraulic fracture simulation methods which are an integral part of this workflow. The workflow is deployed on a dataset from a multi-well pad completed in late 2018 targeting two landing zones in the Vaca Muerta shale play. On calibrated petrophysical and geomechanical model, hydraulic fracture simulations are conducted to map the stimulated rock around the wellbores. Finely gridded base model is utilized to capture the property variation between layers to estimate fracture height. The 3d discrete fracture network (DFN) built for the acreage is utilized to pick the natural fracture characteristics of the layers intersected by the wellbores. The methodology highlights advances over the past modeling approaches by including the variation of discrete fracture network between layers. The hydraulic fracture model in conjunction with reservoir flow simulation is used for history matching the production data. On the history matched model, a design of experiments (DOE) simulation study is conducted to quantify the impact of a wide range of well spacing and stimulation design variables. These simulations are facilitated by the recent deployments of cloud computing. Cloud computing allows parallel running of hundreds of hydraulic fracturing and reservoir simulations, thereby allowing testing of many combinations of stimulation deigns and well spacing and reducing the effective run time from 3 months on a local machine to 1 week on the cloud. Output from the parallel simulations are fitted with a proxy model to finally select the well spacing and stimulation design variables that offer the minimum unit development cost i.e. capital cost-$ per EUR-bbl. The workflow illustrates that stimulation design and well spacing are interlinked to each other and need to be optimized simultaneously to maximize the economics of an unconventional asset. Using the workflow, the team identified development designs which increase EUR of a development area by 50-100% and reduce the unit development cost ($/bbl-EUR) by 10-30%.
Abstract Economic optimization of a reservoir can be extremely tedious and time consuming. It is particularly difficult with many wells, some of which can become non-economic within the simulated time period. These problems can be mitigated by: 1) analyzing the results of a simulation once it has run, or 2) applying injection or production constraints at the well level. An example of option 1 would be integration with a spreadsheet or economic simulation package after the simulation has run. An example of option 2 would be to set a maximum water cut, upon which the well constraints could be changed, or the well could be shut in within the simulation. Both of these methods have drawbacks. If the goal is to account for how changes in a well operating strategy affects other wells, then analysis after the fact requires many runs to sequentially identify and modify well constraints at the correct times and in the correct order. In contrast, applying injection and production constraints to wells is not the same as applying true economic constraints. The objective of this work was to develop an automated method which includes economic considerations within the simulator to decrease the amount of time optimizing a single model and allows more time to analyze uncertainty within the economic decision making process. This study developed automated methods and procedures to include economic calculations within the context of a standard reservoir simulation. The method utilized modifications to available conditional logic features to internally include and export key economic metrics to support appropriate automatic field development changes. This method was tested using synthetic models with different amounts of wells and operating conditions. It was validated using after the fact calculations on a well by well basis to confirm the process. People costs are always among the most significant associated with running a business. Therefore, it is imperative for people to be as efficient and productive as possible. The method presented in this study significantly reduces the amount of time and effort associated with tedious and manual manipulations of simulation models. These savings enable an organization to focus on more value-added activities including, but not limited to, accurately optimizing and estimating of uncertainty associated decisions supported by reservoir simulation.