|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Dashti, Jalal (Kuwait Oil Company) | Al-Ajmi, Bader (Kuwait Oil Company) | Farwan, Hawas (Kuwait Oil Company) | Shoeibi, Ahmad (Geolog International B.V.) | Sanclemente, Milton (Geolog International B.V.) | Martocchi, Alberto (Geolog International B.V.) | Russo, Eliana R. (Geolog International B.V.)
Abstract The economic feasibility of a well drilled in tight carbonates is extremely dependent on the level of fracture permeability; hard and dense carbonate formations may not be considered as net pay without the presence of fractures. The evaluation of fractures is a key to reservoir effectiveness characterization for well drilling, completion, development and stimulation of fractured reservoirs. While knowledge of the geological conditions and regional stress is helpful to estimate the characteristics of the natural fracture system in a given reservoir, the true extent of the natural open fracture system in any specific location is typically unknown. Several methods are available to the industry to identify natural fractures near the wellbore, including acoustic and resistivity image logs. In some cases, the poor-quality results of these techniques do not provide reliable information and such data cannot be available in all the wells. When minor downhole losses are accurately detected, it is possible to locate and characterize the natural open fractures intersected by the drill bit while drilling operations. The differential flow (Flow-out minus Flow-in) and the Active Volume System are continually monitored during drilling and integrated with drilling and hydraulic parameters. These readings are processed in a computer-based, data-acquisition system to form a compensated delta-flow signal that identifies the occurrence of downhole fluid losses. The differential flow is measured accurately through a dedicated Coriolis type flow-meter with a Limit Of Detection up to 10 l/min. By accurately detecting and measuring the downhole micro-losses instantaneously at the surface, the responses would be compared to predefined models for fracture characterization; that enables identification of different types of fractures (open natural, induced fractures). The system can detect very fine micro-fractures that might not be visible with wireline images; fracture density plots can then be created to highlight the fracture concentration along the well. Drilling deep wells in Kuwait is challenging due to high pressure, high-temperature formations, with the Bottom Hole Pressure of +15kpsi and Bottom Hole Temperature of +150 Centigrade degrees. In conventional surface systems, the loss detection relies on the Active Volume System and the Paddle type Flow-out sensor; however, these systems usually fail to identify the minor mud losses associated to open fractures. Especially for active pits with a big surface, it is almost impossible to identify few millimetres of mud level decrease and during fluid transfers, mud conditioning will make the job even more difficult to identify minor losses. With flow paddle type of sensors, the flow out information is not displayed as a calibrated value but rather as a percentage of full scale, which can be difficult to interpret. Instead, dedicated Coriolis type flowmeters properly installed, can identify flow rate changes accurately, regardless of any transfer of mud, water or diesel between pits. By applying this technique, it is possible to identify fractures while drilling in different types of wells, such as vertical, highly deviated and horizontal. The data were validated initially through core and image logs and further applied in next drilling campaigns.
Abstract Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.
Abstract In this case study, we apply a novel fracture imaging and interpretation workflow to take a systematic look at hydraulic fractures captured during thorugh fracture coring at the Hydraulic Fracturing Test Site (HFTS) in Midland Basin. Digital fracture maps rendered using high resolution 3D laser scans are analyzed for fracture morphology and roughness. Analysis of hydraulic fracture faces show that the roughness varies systematically in clusters with average cluster separation of approximately 20' along the core. While isolated smooth hydraulic fractures are observed in the dataset, very rough fractures are found to be accompanied by proximal smoother fractures. Roughness distribution also helps understand the effect of stresses on fracture distribution. Locally, fracture roughness seems to vary with fracture orientations indicating possible inter-fracture stress effects. At the scale of stage lengths however, we see evidence of inter-stage stress effects. We also observe fracture morphology being strongly driven by rock properties and changes in lithology. Identified proppant distribution along the cored interval is also correlated with roughness variations and we observe strong positive correlation between proppant concentrations and fracture roughness at the local scale. Finally, based on the observed distribution of hydraulic fracture properties, we propose a conceptual spatio-temporal model of fracture propagation which can help explain the hydraulic fracture roughness distribution and ties in other observations as well.
Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.
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 The purpose of this paper is to present a technique to estimate hydraulic fracture (HF) length, fracture conductivity, and fracture efficiency using simple and rapid but rigorous reservoir simulation matching of historical production, and where available, pressure. The methodology is particularly appropriate for analysis of horizontal wells with multiple fractures in tight unconventional or unconventional resource plays. In our discussion, we also analyze the differences between the results from decline curve analysis (DCA) approach and the Science Based Forecasting (SBF) results that this work proposes. When we characterize fracture properties with SBF, we can do a better job of forecasting than if we randomly combine fracture properties and reservoir permeability together in a decline-curve trend. The forecasts are significantly different with SBF, therefore fracture characterization plays an important role and SBF uses this characterization to produce different (and better) forecasts.
Abstract The objective of this study was to perform an integrated analysis to gain insight for optimizing fracturing treatment and gas recovery from Marcellus shale. The analysis involved all the available data from a Marcellus Shale horizontal well which included vertical and lateral well logs, hydraulic fracture treatment design, microseismic, production logging, and production data. A commercial fracturing software was utilized to predict the hydraulic fracture properties based on the available vertical and lateral well logs data, diagnostic fracture injection test (DFIT), fracture stimulation treatment data, and microseismic recordings during the fracturing treatment. The predicted hydraulic fracture properties were then used in a reservoir simulation model developed based on the Marcellus Shale properties to predict the production performance. In this study, the rock mechanical properties were estimated from the well log data. The minimum horizontal stress, instantaneous shut-in pressure (ISIP), process zone stress (PZS), and leak-off mechanism were determined from DFIT analysis. The stress conditions were then adjusted based on the results of microseismic interpretations. Subsequently, the results of the analyses were used in the fracturing software to predict the hydraulic fracture properties. Marcellus Shale properties and the predicted hydraulic fracture properties were used to develop a reservoir simulation model. Porosity, permeability, and the adsorption characteristics were estimated from the core plugs measurements and the well log data. The image logs were utilized to estimate the distribution of natural fractures (fissures). The relation between the formation permeability and the fracture conductivity and the net stress (geomechanical factors) were obtained from the core plugs measurements and published data. The predicted production performance was then compared against production history. The analysis of core data, image logs, and DFIT confirmed the presence of natural fractures in the reservoir. The formation properties and in-situ stress conditions were found to influence the hydraulic fracturing geometry. The hydraulic fracture properties are also impacted by stress shadowing and the net stress changes. The production logging tool results could not be directly related to the hydraulic fracture properties or natural fracture distribution. The inclusion of the stress shadowing, microseismic interpretations, and geomechanical factors provided a close agreement between the predicted production performance and the actual production performance of the well under study.