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Free data from the first Permian Hydraulic Fracturing Test Site is available online and reports from the second test site will be available this summer. Those reports offer a unique look at fracturing in the Midland and Delaware Basins, using nearly every diagnostic test an engineer can think of and analysis by technology leaders including Occidental and Shell among the partners. Because the US government shares the cost with industry partners, the data are made publicly available after a period during which the companies that pay half the cost have exclusive access to it. The data posted draw on work begun more than 5 years ago. It is available on the National Energy Technology Laboratory's EDX data sharing site (details below). The files offer processed data from testing done at an 11-well pad in the Midland Basin dating back to 2015, said Gary Covatch, a petroleum engineer at the US Department of Energy.
Abstract During drilling oil, gas, or geothermal wells, the temperature difference between the formation and the drilling fluid will cause a temperature change around the borehole, which will influence the wellbore stresses. This effect on the stresses tends to cause wellbore instability in high temperature formations, which may lead to some problems such as formation break down, loss of circulation, and untrue kick. In this research, a numerical model is presented to simulate downhole temperature changes during circulation then simulate its effect on fracture pressure gradient based on thermo-poro-elasticity theory. This paper also describes an incident occurred during drilling a well in Gulf of Suez and the observations made during this incident. It also gives an analysis of these observations which led to a reasonable explanation of the cause of this incident. This paper shows that the fracture pressure decreases as the temperature of wellbore decreases, and vice versa. The research results could help in determining the suitable drilling fluid density in high-temperature wells. It also could help in understanding loss and gain phenomena in HT wells which may happen due to thermal effect. The thermal effect should be taken into consideration while preparing wellbore stability studies and choosing mud weight of deep wells, HPHT wells, deep water wells, or wells with depleted zones at high depths because cooling effect reduces the wellbore stresses and effective FG. Understanding and controlling cooling effect could help in controlling the reduction in effective FG and so avoid lost circulation and additional unnecessary casing points.
Al-Nakhli, Ayman R. (Saudi Aramco) | Tariq, Zeeshan (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum and Minerals)
Abstract Commercial volumes of hydrocarbon production from tight unconventional reservoirs need massive hydraulic fracturing operations. Tight unconventional formations are typically located inside deep and over-pressured formations where the rock fracture pressure with slickwater becomes so high because of huge in situ stresses. Therefore, several lost potentials and failures were recorded because of high pumping pressure requirements and reservoir tightness. In this study, thermochemical fluids are introduced as a replacement for slickwater. These thermochemical fluids are capable of reducing the rock fracture pressure by generating micro-cracks and tiny fractures along with the main hydraulic fractures. Thermochemical upon reaction can generate heat and pressure simultaneously. In this study, several hydraulic fracturing experiments in the laboratory on different synthetic cement samples blocks were carried out. Cement blocks were made up of several combinations of cement and sand ratios to simulate real rock scenarios. Results showed that fracturing with thermochemical fluids can reduce the breakdown pressure of the cement blocks by 30%, while applied pressure was reduced up to 88%, when using thermochemical fluid, compared to slickwater. In basins with excessive tectonic stresses, the current invention can become an enabler to fracture and stimulate well stages which otherwise left untreated. A new methodology is developed to lower the breakdown pressure of such reservoirs, and enable fracturing. Keywords: Unconventional formation; breakdown pressure; thermochemicals; micro fractures.
James Blaney is an engineer on a hydraulic fracturing fleet for Liberty Oilfield Services, and is based in the Permian Basin. He holds a bachelor's degree in petroleum engineering from the Colorado School of Mines (CSM). While at CSM, he was an active member of the CSM SPE Student Chapter. He volunteered regularly at fundraising events and was a member and captain of the CSM PetroBowl team.
Se, Yegor (Chevron ETC) | Sullivan, Michael (Chevron Canada) | Tohidi, Vahid (Chevron Canada) | Lazorek, Michael (Chevron Canada) | Attia, Ahmed (Ziebel US) | Chen, Phillip (Ziebel US) | Abbassi, Linda (Openfield Technology) | Schoepf, Virginie (Openfield Technology)
Abstract The well design with long lateral section and multistage frac completion has been proven effective for development of the unconventional reservoirs. Top-tier well production in unconventional reservoir can be achieved by optimizing hydraulic completion and stimulation design, which necessitates an understanding of flow behavior and hydrocarbon contribution allocation. Historically, conventional production logging (PL) surveys were scarcely used in unconventional reservoirs due to limited and often expensive conveyance options, as well as complicated and non-unique inflow interpretations caused by intricate and changing multiphase flow behavior (Prakash et al., 2008). The assessment of the cluster performance gradually shifted towards distributed acoustic (DAS) and temperature (DTS) sensing methods using fiber optics cable, which continuously gained popularity in the industry. Fiber optics measurements were anticipated to generate production profiles along the lateral with sub-cluster resolution to assist with optimal completions design selection. Encapsulation of the fiber in the carbon rod provided alternative conveyance method for retrievable DFO measurements, which gained popularity due to cost-efficiency and operational convenience (Gardner et al., 2015). Recent utilization of micro-sensor technology in PL tools, (Abbassi et al, 2018, Donovan et al, 2019) allowed dramatic reduction of the size and the weight of the PL toolstring without compromising wellbore coverage by sensor array. Such ultra-compact PL toolstring could utilize the carbon rod as a taxi and provide mutually beneficial and innovative surveillance combination to evaluate production profile in the unconventional reservoirs. Array holdup and velocity measurements across wellbore from PL would reveal more details regarding multi-phase flow behavior, which could be used for cross-validation and constraining of production inflow interpretation based on DFO measurements. This paper summarizes the lessons learned, key observations and best practices from the unique 4 well program, where such innovative combination was tested in gas rich Duvernay shale reservoir.
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.
When a panel of fracturing technology leaders was asked if classic physics-based engineering matters in engineering fracturing, the answer was a qualified "sometimes." The group of three engineers speaking at the start of the SPE Hydraulic Fracturing Technology Conference was not going to dismiss the need for physics-based modeling. Still, applying the physics of flow in a complex, fractured reservoir sounded like a wrong turn. To explain further, Cameron Rempel, vice president for subsurface engineering for Occidental Petroleum, compared analysis at the fracture level to trying to understand rush hour traffic by tracking each person as they pack up in their cubicle and head for their car at the end of the day. That example, which will someday again represent office reality, is both incredibly hard to measure and analyze and does not offer a direct path to answer an analogous question that matters to oil producers: How can we measure the time it takes for all those cars to flow out of downtown and find ways to speed them up?
Nicholson, A. Kirby (Pressure Diagnostics Ltd.) | Bachman, Robert C. (Pressure Diagnostics Ltd.) | Scherz, R. Yvonne (Endeavor Energy Resources) | Hawkes, Robert V. (Cordax Evaluation Technologies Inc.)
Abstract Pressure and stage volume are the least expensive and most readily available data for diagnostic analysis of hydraulic fracturing operations. Case history data from the Midland Basin is used to demonstrate how high-quality, time-synchronized pressure measurements at a treatment and an offsetting shut-in producing well can provide the necessary input to calculate fracture geometries at both wells and estimate perforation cluster efficiency at the treatment well. No special wellbore monitoring equipment is required. In summary, the methods outlined in this paper quantifies fracture geometries as compared to the more general observations of Daneshy (2020) and Haustveit et al. (2020). Pressures collected in Diagnostic Fracture Injection Tests (DFITs), select toe-stage full-scale fracture treatments, and offset observation wells are used to demonstrate a simple workflow. The pressure data combined with Volume to First Response (Vfr) at the observation well is used to create a geometry model of fracture length, width, and height estimates at the treatment well as illustrated in Figure 1. The producing fracture length of the observation well is also determined. Pressure Transient Analysis (PTA) techniques, a Perkins-Kern-Nordgren (PKN) fracture propagation model and offset well Fracture Driven Interaction (FDI) pressures are used to quantify hydraulic fracture dimensions. The PTA-derived Farfield Fracture Extension Pressure, FFEP, concept was introduced in Nicholson et al. (2019) and is summarized in Appendix B of this paper. FFEP replaces Instantaneous Shut-In Pressure, ISIP, for use in net pressure calculations. FFEP is determined and utilized in both DFITs and full-scale fracture inter-stage fall-off data. The use of the Primary Pressure Derivative (PPD) to accurately identify FFEP simplifies and speeds up the analysis, allowing for real time treatment decisions. This new technique is called Rapid-PTA. Additionally, the plotted shape and gradient of the observation-well pressure response can identify whether FDI's are hydraulic or poroelastic before a fracture stage is completed and may be used to change stage volume on the fly. Figure 1: Fracture Geometry Model with FDI Pressure Matching Case studies are presented showing the full workflow required to generate the fracture geometry model. The component inputs for the model are presented including a toe-stage DFIT, inter-stage pressure fall-off, and the FDI pressure build-up. We discuss how to optimize these hydraulic fractures in hindsight (look-back) and what might have been done in real time during the completion operations given this workflow and field-ready advanced data-handling capability. Hydraulic fracturing operations can be optimized in real time using new Rapid-PTA techniques for high quality pressure data collected on treating and observation wells. This process opens the door for more advanced geometry modeling and for rapid design changes to save costs and improve well productivity and ultimate recovery.
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.