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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.
Pore pressure is an important driver in many aspects of petroleum exploration and production including prospect screening, drilling, completion, and production. However, in low permeability unconventional reservoirs it is only possible to sample reservoir pressure one point at a time. This leads to the question of how we interpolate between these pressure measurements to derive a 3D pore pressure model that can be predictive for drilling operations, and useful for completions and reservoir engineering models.
In this paper we will discuss a workflow to integrate point data pressure measurements such as the Diagnostic Fracture Injection Test (DFIT), with pressure indicators like mud weight (MW), and multiple empirical log based pore pressure prediction methods into one 3D model of the basin. For log based pore prediction, the Eaton-Yale, and Eaton methods were used to calculate continuous pore pressure profiles across the entire stratigraphic section logged. These pore pressure prediction methods were calibrated to DFIT measurements were the data was available. Next, the calculated 1D pore pressure logs were imported into a fine grid 3D stratigraphic model and geostatistically interpolated throughout the model. Then after rigorous quality control, drill stem test (DST) and MW measurements from thousands of wells were incorporated into the 3D model to validate the petrophysical based pore pressure calculation. The result of this workflow is a 3D model populated with pore pressure and pore pressure gradients at each discrete cell.
The resulting model can be used to understand different aspects of the reservoir by different disciplines. Geoscientists can use this model to understand the geological causes of overpressure, hydrocarbon maturation, or sealing stratigraphy. When combined with 3D rock property models, the pressure model could help us to identify the overpressured geological zones.
In addition to the geological uses, this information can be used by other disciplines in their workflows. Drilling engineers can use the estimated pressure as an input for wellbore stability models to enhance well planning. Completions engineers can use the outputs in their hydraulic fracture models, and reservoir engineers can import this data into their production models. Most importantly having everyone using the same pore pressure model fosters better integration, communication, and understanding of the reservoir.
Haustveit, Kyle (Devon Energy) | Elliott, Brendan (Devon Energy) | Haffener, Jackson (Devon Energy) | Ketter, Chris (Devon Energy) | O'Brien, Josh (Devon Energy) | Almasoodi, Mouin (Devon Energy) | Moos, Sheldon (Devon Energy) | Klaassen, Trevor (Devon Energy) | Dahlgren, Kyle (Devon Energy) | Ingle, Trevor (Devon Energy) | Roberts, Jon (Devon Energy) | Gerding, Eric (Devon Energy) | Borell, Jarret (Devon Energy) | Sharma, Sundeep (Devon Energy) | Deeg, Wolfgang (Formerly Devon Energy)
Over the past decade the shale revolution has driven a dramatic increase in hydraulically stimulated wells. Since 2010, hundreds of thousands of hydraulically fractured stages have been completed on an annual basis in the US alone. It is well known that the geology and geomechanical features vary along a lateral due to landing variations, structural changes, depletion impacts, and intra-well shadowing. The variations along a lateral have the potential to impact the fluid distribution in a multi-cluster stimulation which can impact the drainage pattern and ultimately the economics of the well and unit being exploited. Due to the lack of low-cost, scalable diagnostics capable of monitoring cluster efficiency, most wells are completed using geometric cluster spacing and the same pump schedule across a lateral with known variations.
A breakthrough patent-pending pressure monitoring technique using an offset sealed wellbore as a monitoring source has led to advancements in quantifying cluster efficiencies of hydraulic stimulations in real-time. To date, over 1,500 stages have been monitored using the technique. Sealed Wellbore Pressure Monitoring (SWPM) is a low-cost, non-intrusive method used to evaluate and quantify fracture growth rates and fracture driven interactions during a hydraulic stimulation. The measurements can be made with only a surface pressure gauge on a monitor well.
SWPM provides insight into a wide range of fracture characteristics and can be applied to improve the understanding of hydraulic fractures in the following ways: Qualitative cluster efficiency/fluid distribution Fracture count in the far-field Fracture height and fracture half-length Depletion identification and mitigation Fracture model calibration Fracture closure time estimation
Qualitative cluster efficiency/fluid distribution
Fracture count in the far-field
Fracture height and fracture half-length
Depletion identification and mitigation
Fracture model calibration
Fracture closure time estimation
The technique has been validated using low frequency Distributed Acoustic Sensing (DAS) strain monitoring, microseismic monitoring, video-based downhole perforation imaging, and production logging. This paper will review multiple SWPM case studies collected from projects performed in the Anadarko Basin (Meramec), Permian Delaware Basin (Wolfcamp), and Permian Delaware Basin (Leonard/Avalon).
Abstract When using wireline log to characterize formation properties for an area we often run into incomplete datasets. One way to address this lack of data is to create synthetic curves to use in the analysis. This paper will cover workflows to generate synthetic photoelectric (PE) and unconfined compressive strength (UCS) logs. Modern well log data sets usually include PE logs which provides important information about the lithology of the formations the wellbore intersects. However, many legacy wells do not have PE logs that we need in order to understand the lithology of these formations. Similarly, it is important to obtain UCS data from mechanical failure tests done to core samples to determine the strength of the rock. Obtaining and testing core samples for the entire zones of interest is both expensive and time consuming. Synthetic well logs can be a reliable and cheaper alternative to predicting PE and UCS values rather than running a new set of logs or coring and testing the whole zone of interest. In the first workflow, Synthetic PE logs were generated using wireline logs from over a hundred wells that included gamma ray, density, neutron, resistivity logs and volume of clay. The data was randomly partitioned into a 70:30 split for training and validation data set respectively. Model competition among a suite of machine learning algorithms such as Linear Regression, Artificial Neural Networks (ANNs), Decision Trees, Gradient Boosting and Random Forest was used to select the best algorithm based on the least average squared error (ASE) of the validation dataset. In the second workflow, UCS data was generated using wireline logs and core rebound hammer data from fourteen wells including gamma ray, density, porosity, neutron, clay volume, kerogen volume, compressional slowness, shear slowness, Young's modulus (static and dynamic) and Poison's ratio. Variable clustering was used to remove collinearity, decrease variable redundancy, and choose the best variables for analysis. Cluster analysis was performed on the chosen variables to identify factors that differentiate data segments from the population. The data was randomly portioned into a 70:30 training and validation split and model competition amongst the suite of machine learning algorithms mentioned above was used to select the champion model based on the least ASE of the validation dataset. Results show that neural networks and random forests generated the best prediction of UCS and synthetic PE logs compared to other machine learning algorithms used.
Summary There has been considerable discussion regarding the possibility and likelihood that vertical nodal plane dip-slip events commonly observed in microseismic data are actually horizontal bedding-plane slip events. The vertical nodal plane is roughly parallel to the maximum stress, with no simple explanation for the shear stress necessary to cause it to slip. The horizontal plane is more easily explained by the shear stress concentration at the tip of a vertically propagating hydraulic fracture. Supporting this interpretation is the fact that the range of azimuthal directions from vertical nodal planes is often more complex than commonly occurs with other nodal plane orientations. The implication of this evidence is that these events occur at the termination of vertical hydraulic fracture growth or at layer boundaries between rocks with different mechanical properties. To investigate this hypothesis, we examined multiple data sets through moment tensor inversion using the horizontal-slip interpretation. The horizontal-slip interpreted events were then analyzed using a geomechanical model of the area. The magnitude and degree of layer varying mechanical properties along with the local geologic dip support the correlation of horizontal nodal plane orientation with bedding plane slip theory. Also observed in the data sets were strike-slip type events which correlated with known fracture orientations from geomechanical logs. Introduction Early attempts at characterizing source orientations of microseismic events assumed a double-couple mechanism due to limited observations, such as being constrained to a single vertical monitoring array. These results typically revealed strike-slip type mechanisms interpreted as slip on pre-existing fractures (Rutledge and Phillips 2003) or vertical dip-slip mechanisms that suggested the possibility of bedding plane slip (Rutledge et. al. 2014). With the prevalence of multiple arrays, improved characterization of these sources is now possible. Of the two data sets we examine, with nearly full focal sphere coverage, one reveals dominant strike-slip failure on a set of pre-existing natural fractures, and the other is dominated by events that we interpret as bedding plane slip. Theory Microseismic events have traditionally been associated with pore pressure diffusion and the associated failure resulting from the reduction in normal stress on fracture planes in a triaxial stress environment. In tight unconventional reservoirs with very low leak-off, direct communication of the hydraulic fracture with the natural fractures likely leads to slip along those fractures.
Zhou, Xuejun (Reservoir Geomechanics and Seismicity Research Group University of Oklahoma) | Ghassemi, Ahmad (Reservoir Geomechanics and Seismicity Research Group University of Oklahoma) | Riley, Spencer (Devon Energy, Inc.) | Roberts, Jon (Devon Energy, Inc.)
ABSTRACT: Biot’s effective stress law has been proved to be very useful in describing the effect of pore fluid pressure on the mechanical response of rocks and understanding the poroelastic effects in unconventional shale or mudstone formations. In this work, six mudstone source rock samples from unconventional reservoir wells have been tested for measuring their grain bulk moduli, bulk moduli, and the stress dependent Biot’s effective stress coefficients. Grain bulk moduli of these samples range from 60 GPa to 90 GPa. Biot’s effective stress coefficients for all the samples decrease from 0.90 or higher to 0.70 or lower with increasing effective stress illustrating the stress dependency of Biot’s coefficient. Furthermore, the different values of Biot’s coefficients along the vertical and horizontal directions indicate that Biot’s coefficient is indeed a tensor quantity for anisotropic mudstone source rocks, so that pore pressure changes not only modify the normal stress but also induce shear stress changes. Experiments also show that mudstone source rock samples expand when nitrogen gas is used as the testing fluid (gas absorption), and the degree of such expansion is related to the absorbed gas quantity and organic matter content.
The behavior of rocks under the combined effects of confining stress and pore pressure is important for stability analysis, geotechnical and petroleum engineering, and sequestration of carbon dioxide, etc (Chen, 2011; Hunt, 1990; Ghassemi et al., 2009; Ma et al., 2016/7; Rutqvist, 2012; Warpinski and Teufel, 1992; Wu et al., 2010; Zhou et al., 2010). The concept of effective stress has been proved to be very useful in describing the effect of pore fluid pressure on the mechanical response of porous earth materials, and this effective stress concept was firstly introduced by Terzaghi (1936) for soil, and then was modified by Biot (1941) for rock. The Biot’s effective stress law is usually given as: