Inferring Hydraulic Connectivity of Induced Fractures in the Near-wellbore Region Using DAS-recorded Tube Waves Excited by Perforation Shots

We present a novel use of tube waves exited by perforation (or “perf”) shots and recorded on distributed acoustic sensing (DAS) to infer and compare the hydraulic connectivity of induced fractures near the wellbore on a stage-by-stage basis. Evaluating the fracture connectivity near the wellbore is critical since it controls the flow of the hydrocarbons from the formation to the wellbore. Currently, there are no established methods used to assess this property. However, we discuss how tube wave decay rates can be used to infer relative differences in fracture connectivity between stages and, through field observations on DAS, demonstrate the correlation between decay rates and frac effectiveness. Additionally, we consider other potential uses of this data in unconventional wells such as assessing plug integrity and constraining fracture geometry with Krauklis waves. DAS data is commonly acquired during the perf shots but primarily for fiber depth calibration purposes and has not been well studied. Our work illustrates the untapped potential of this data and how it can be easily repurposed to bring new insights about fracture characteristics in the near-wellbore region.

Applying Slim Dipole Sonic Tool to Evaluate Hydraulic Fracture Geometry and Orientation Using the Differential Sonic Method

Successful hydraulic fracturing is critical for hydrocarbon recovery from tight reservoirs. Fracture geometry is one essential quality indicator of the created fracture. The geometry provides information about the size of the created fracture and containment and verifies the pre-job modeling. Different techniques are applied to determine fracture geometry, and each has its own advantages and limitations. Due to its simplicity, the radioactive tracer log is commonly used to determine fracture placement and fracture height. Its main drawbacks include shallow depth of investigation, time dependency, and the requirement for multiple interventions for multistage fracturing operations. The crosswell microseismic technique probes a larger volume and it is potentially capable of providing fracture height, length, and orientation. Operational complexity and long processing turnaround time are the main challenges of this technique. Time-lapse shear slowness anisotropy analysis is an effective method to determine hydraulic facture height and orientation. In this technique, the shear slowness anisotropy is recorded before and after the fracture is created. The observed shear anisotropy difference indicates the intervals where the fractures were created, allowing these intervals lengths to be measured. Combining this analysis with gyroscopic data allows determining the fracture orientations. Compared to a tracer log, the differential casedhole sonic anisotropy (DCHSA) has a deeper depth of investigation, and it is time independent. Thus, the repeated log can be acquired at the end of the multistage fracturing operations. Compared to the microseismic technique, this new technique provides more precise fracture height and orientation. The new generation slim dipole sonic technology of 2.125-in. diameter extends the applicability of the DCHSA technique to smaller casing sizes. The shear differential method was applied to a vertical well that targeted the Athel formation in the south of the Sultanate of Oman. This formation is made of silicilyte and is characterized by very low permeability of about 0.01 md on average. Thus, hydraulic fracturing plays a critical role for the economic oil recovery in this reservoir. Aiming to achieve a better zonal contribution, the stimulation design was changed from a limited number of large fractures to an extensive multistage fracturing design in the subject well. Sixteen hydraulic fracturing stages were planned. The DCHSA was applied to provide accurate and efficient fracture geometry evaluation. The DCHSA accurately identified fracture intervals and their corresponding heights and orientations. This enabled effectively determining the created fracture quality and helped explain the responses of the production logs that were recorded during the well test. This study provided a foundation for the placement and completion design of the future wells in the subject reservoir. It particularly revealed adequate fracturing intervals and the optimum number of stages required to achieve optimum reservoir coverage and avoid vertical overlapping.

Fracture Geometry Calibration Using Multiple Surveillance Techniques

An accurate Mechanical Earth Model (MEM) is of vital importance in tight gas reservoirs where hydraulic fracturing is the only way to produce hydrocarbons economically. The Barik tight gas reservoir is the main target in Khazzan and Ghazeer Fields at the Sultanate of Oman (Rylance et al., 2011). This reservoir consists of multiple low-permeability sandstone layers interbedded with marine shales. A good understanding of the fracture propagation in such a reservoir has a major effect on completion and fracturing design. The MEM derived from sonic logs and calibrated with core data needs to be further validated by independent measurements of the fracturing geometry. Multiple surveillance techniques have been implemented in the Barik reservoir to validate the MEM and to match observations from hydraulic fracturing operations. These techniques include closure interpretation using a wireline deployed formation testing assembly, the use of mini-frac injection tests with deployed bottomhole pressure gauges, execution of post injection time-lapse temperature logging, the injection of radioactive tracers, associated production logging, subsequent pressure transient analysis and other techniques. A cross-disciplinary team worked with multiple sources of data to calibrate the MEM with the purpose of delivering a high-confidence prediction of the created fracture geometry, which honors all available surveillance data. In turn, this validation approach provided a solid basis for optimization of the completion and fracturing design, in order to optimally exploit this challenging reservoir and maximize the economic returns being delivered. For example, combination of stress testing with radioactive tracers provided confidence in stress barriers in this multilayered reservoir. Pressure transient analysis allowed to calibrate mechanical model to match fracturing half-length that is contributing to production. This paper provides extensive surveillance examples and workflows for data analysis. Surveillance of this degree in the same well is uncommon because of the associated time and cost. However, it provides unique value for understanding the target reservoir. This paper demonstrates the Value Of Information (VOI) that can be associated with such surveillance and provides a concrete and practical example that can be used for the justification of future surveillance programs associated with the hydraulic fracturing operations.

Supervised Learning Predictive Models for Automated Fracturing Treatment Design: A Workflow Based on Algorithm Comparison and Multiphysics Model Validation

Diagnostic pumping techniques are used routinely in proppant fracturing design. The pumping process can be time consuming; however, it yields technical confidence in treatment and productivity optimization. Recent developments in data analytics and machine learning can aid in shortening operational workflows and enhance project economics. Supervised learning was applied to an existing database to streamline the process and affect the design framework. Five classification algorithms were used for this study. The database was constructed through heterogeneous reservoir plays from the injection/falloff outputs. The algorithms used were support vector machine, decision tree, random forest, multinomial, and XGBoost. The number of classes was sensitized to establish a balance between model accuracy and prediction granularity. Fifteen cases were developed for a comprehensive comparison. A complete machine learning framework was constructed to work through each case set along with hyperparameter tuning to maximize accuracy. After the model was finalized, an extensive field validation workflow was deployed. The target outputs selected for the model were crosslinked fluid efficiency, total proppant mass, and maximum proppant concentration. The unsupervised clustering technique with t-SNE algorithm that was used first lacked accuracy. Supervised classification models showed better predictions. Cross-validation techniques showed an increasing trend of prediction accuracy. Feature selection was done using one-variable-at-a-time (OVAT) and a simple feature correlation study. Because the number of features and the dataset size were small, no features were eliminated from the final model building. Accuracy and F1 score calculations were used from the confusion matrix for evaluation, XGBoost showed excellent results with an accuracy of 74 to 95% for the output parameters. Fluid efficiency was categorized into three classes and yielded an accuracy of 96%. Proppant concentration and proppant mass predictions showed 77% and 86% accuracy, respectively, for the six-class case. The combination of high accuracy and fine granularity confirmed the potential application of machine learning models. The ratio of training to testing (holdout) across all cases ranged from 80:20 to 70:30. Model validations were done through an inverse problem of predicting and matching the fracture geometry and treatment pressures from the machine learning model design and the actual net pressure match. The simulations were conducted using advanced multiphysics simulations. The advantages of this innovative design approach showed four areas of improvement: reduction in polymer consumption by 30%, reduction of the flowback time by 25%, reduction of water usage by 30%, and enhanced operational efficiency by 60 to 65%.

Integrated Hydraulic Fracture Geometry Evaluation Based on Pre-Cambrian Tight Silicylate Reservoir in South Oman Salt Basin

Pre-Cambrian South Oman tight silicilyte reservoirs are very challenging for the development due to poor permeability less than 0.1 mD and laminated texture. Successful hydraulic fracturing is a key for the long commercial production. One of the main parameter for frac planning and optimization is fracture geometry. The objective of this study was summarizing results comparison from different logging methods and recommended best practices for logging program targeting fracture geometry evaluation. The novel method in the region for hydraulic fracture height and orientation evaluation is cross-dipole cased hole acoustic logging. The method allows to evaluate fracture geometry based on the acoustic anisotropy changes after frac operations in the near wellbore area. The memory sonic log combined with the Gyro was acquired before and after frac operations in the cased hole. The acoustic data was compared with Spectral Noise log, Chemical and Radioactive tracers, Production Logging and pre-frac model. Extensive logging program allow to complete integrated evaluation, define methods limitations and advantages, summarize best practices and optimum logging program for the future wells. The challenges in combining memory cross-dipole sonic log and gyro in cased hole were effectively resolved. The acoustic anisotropy analysis successfully confirms stresses and predominant hydraulic fractures orientation. Fracture height was confirmed based on results from different logging methods. Tracers are well known method for the fracture height evaluation after hydraulic frac operations. The Spectral Noise log is perfect tool to evaluate hydraulically active fracture height in the near wellbore area. The combination of cased hole acoustic and noise logging methods is a powerful complex for hydraulic fracture geometry evaluation. The main limitations and challenges for sonic log are cement bond quality and hole conditions after frac operations. Noise log has limited depth of investigation. However, in combination with production and temperature logging provides reliable fit for purpose capabilities. The abilities of sonic anisotropy analysis for fracture height and hydraulic fracture orientation were confirmed. The optimum logging program for fracture geometry evaluation was defined and recommended for replication in projects were fracture geometry evaluation is required for hydraulic fracturing optimization.

Utilizing Microseismic to Monitor Fracture Geometry in a Horizontal Well in a Tight Sandstone Formation

This paper presents the use of real-time microseismic (MS) monitoring to understand hydraulic fracturing of a horizontal well drilled in the minimum stress direction within a high-temperature high-pressure (HTHP) tight sandstone formation. The well achieved a reservoir contact of more than 3,500 ft. Careful planning of the monitoring well and treatment well setup enabled capture of high quality MS events resulting in useful information on the regional maximum horizontal stress and offers an understanding of the fracture geometry with respect to clusters and stage spacing in relation to fracture propagation and growth. The maximum horizontal stress based on MS events was found to be different from the expected value with fracture azimuth off by more than 25 degree among the stages. Transverse fracture propagation was observed with overlapping MS events across stages. Upward fracture height growth was dominant in tighter stages. MS fracture length and height in excess of 500 ft and 100 ft, respectively, were created for most of the stages resulting in stimulated volumes that are high. Bigger fracture jobs yielded longer fracture length and were more confined in height growth. MS events fracture lengths and heights were found to be on average 1.36 and 1.30 times, respectively, to those of pressure-match.

Integration of Geoscience and Engineering Concepts to Account for Natural Fractures in Fluid Flow within Shale Reservoirs

Unconventional shale reservoirs are characterized by low porosity and ultra-low permeability. Natural fractures are known to be present and considered a critical factor for the enhanced post-stimulation productivity. Accounting for natural fractures with existing techniques has not been widely adopted owing to their complexity or lack of validation. Ongoing research efforts are striving to understand how natural fractures can be accounted for and accurately modeled in fluid flow of the subject reservoirs. This study utilized Eagle Ford well data comprising reservoir properties, stimulation metrics, production, microseismicity and permeability measurements from a core plug. The methodology comprised use of production data to extract a linear flow regime parameter. This was coupled with fracture geometry, predicted from hydraulic fracture modeling and microseismicity, to estimate the system permeability. From interpreting microseismic events as slips on critically stressed natural fractures, explicit modeling incorporating a discrete fracture network (DFN) assumed activated natural fractures supplement conductive reservoir contact area. Thus, allowed the estimation of matrix permeability. For validation, the aforementioned was compared with core plug permeability measurements. Results from modeling of planar hydraulic fractures, with microseismicity as validation, predicted planar fracture geometry which when coupled with the linear flow parameter resulted in a system permeability. Incorporation of DFNs to account for activated natural fractures yielded matrix permeability in picodarcy range. A review of laboratory permeability measurements exhibited stress dependence with the value at the maximum experimental confining pressure of 4000 psi in the same range as the computed system permeability. However, the confining pressures used in the experiments were less than the in situ effective stress. Correction for representative stress yielded an ultra-low matrix permeability in the same range as the DFN-based picodarcy matrix permeability. Thus, supporting the adopted drainage architecture and often suggested role of natural fractures in shale reservoir fluid flow. This study presents a multi-discipline workflow to account for natural fractures, and contributes to understanding that will improve laboratory petrophysics and the overall reservoir characterization of the subject reservoirs. Given that the Eagle Ford is an analogue of emerging shales elsewhere, results from this study can be widely adopted.

Fracturing Fluid Design: A Closer Look at Breaker and Surfactant Selection

Guar and its derivatives are the most commonly used gelling agents for fracturing fluids. At high temperature, higher polymer loadings are required to maintain sufficient viscosity for proper proppant carry and creating the fracture geometry. To minimize fracturing fluids damage and optimize fracture conductivity, it is necessary to design a fluid that is easy to clean up by ensuring proper breaking and sufficiently low surface tension for flow back. Therefore, breakers and surfactants must be carefully selected and optimally dosed to ensure the success of fracturing treatments. In this study, two fracturing fluids were evaluated for moderate to high temperature applications with a focus on post-treatment cleanup efficiency. The first is a guar-based fluid with a borate crosslinker evaluated at 280°F and the second is a CMHPG-based fluid with a zirconate crosslinker evaluated at 320°F. The shear viscosities of both fluids were tested with a live sodium bromate breaker, a polymer encapsulated ammonium persulfate breaker and a dual breaker system combining the two breakers. Different anionic and nonionic surfactant chemistries (aminosulfonic acid and alcohol based) were investigated by measuring surface tension of the surfactant solutions at different concentrations. The compatibility of the surfactants with other fracturing fluid additives and their adsorption in Berea sandstone was also investigated. Finally, the damage caused by leak-off for each fracturing fluid was simulated by using coreflooding experiments and Berea sandstone core plugs. Lab results showed the guar and CMHPG fluids maintained sufficient viscosity for the first two hours at baseline, respectively. The encapsulated breaker proved to be effective in delaying the breaking of the fracturing fluids. The dual breaker system was the most effective and the loading was optimized for each tested temperature to provide the desired viscosity profile. Two of the examined surfactants were effective in lowering surface tension (below 30 dyne/cm) and were stable for all tested temperatures. The guar broken fluid showed better regained permeability (up to 94%) when compared to the CMHPG (up to 53%) fluid for Berea sandstone. This paper outlines a methodical approach to selecting and optimizing fracturing fluid chemical additives for better post-treatment cleanup and subsequent well productivity.

Intelligent Fracture Diagnostic Procedure Using Smart Microchip Proppants Data

Unconventional oil and gas reservoir development requires an understanding of the geometry and complexity of hydraulic fractures. The current categories of fracture diagnostic approaches include methods for near-wellbore (production and temperature logs, tracers, borehole imaging) and far-field techniques (micro-seismic fracture mapping). These techniques provide an indirect and/or interpreted fracture geometry. Therefore, none of these methods consistently provides a fully detailed and accurate description of the character of created hydraulic fractures. This study proposes a novel approach that uses direct data from the injected fine size and battery-less Smart MicroChip Proppants (SMPs) to map the fracture geometry. This novel approach enables direct, fast, and smart of the received high-resolution geo-sensor data from the SMPs collected in high pressure and high-temperature environment and maps the fracture network using the proposed Intelligent and Integrated Fracture Diagnostic Platform (IFDP), which is a closed-loop architecture and is based on multi-dimensional projection, unsupervised clustering, and surface reconstruction. Affine transformation and a shallow ANN are integrated to control the stochasticity of clustering. IFDP proves its efficacy in fracture diagnostics for 3 in-house design synthetic fracture networks, with 100% consistency, rated "fairly satisfied" to "highly satisfied" in prediction capability, and between 85-100% in execution robustness. The integration of the couple affine transformation-ANN increases the performance of unsupervised clustering in IFDP.