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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.
Desroches, Jean (Rocks Expert) | Peyret, Emilie (Schlumberger) | Gisolf, Adriaan (Schlumberger) | Wilcox, Ailsa (Schlumberger) | Di Giovanni, Mauro (Schlumberger) | de Jong, Aernout Schram (Schlumberger) | Sepehri, Siavash | Garrard, Rodney (Nagra) | Giger, Silvio (Nagra)
Abstract As part of the Sectoral Plan for Deep Geological Repositories, three candidate sites are currently examined by a focused geological exploration program in Northeastern Switzerland. The program involves 3D seismic surveys and drilling of at least two deep boreholes at each site. Stress testing is being undertaken with a wireline formation testing tool in each borehole (around 20 stress tests per borehole). Improvements in the toolstring were introduced step by step to sharpen the range of the stress estimates and enable 100% coverage of the desired lithological column. This is the first time that a single toolstring with three packers has been run to perform the complete combination of sleeve fracturing, hydraulic fracturing and sleeve reopening tests. A dedicated stress testing protocol was developed to ensure the most robust estimate of the stress in a large variety of formations. A detailed planning process has been developed to maximize the success rate and coverage of stress test stations, integrating all available information as it becomes available. A review of the techniques enabled by the new toolstring for estimating the closure stress from a stress test, especially in low-permeability formations, is presented, and detailed stress testing examples are provided. Preliminary comparison between the stress estimates for the first two boreholes in the campaign are shown.
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.
Chen, Qian (China University of Petroleum (East China)) | Zhang, Feng (China University of Petroleum (East China)) | Tian, Lili (China University of Petroleum (East China)) | Zhang, Xiaoyang (China University of Petroleum (East China)) | Li, Xianghui (Isotope Research Institute of Henan Academy of Sciences Co. Ltd.) | Fang, Qunwei (China University of Petroleum (East China)) | Fan, Junting (China University of Petroleum (East China))
Abstract The evaluation of carbonate rocks with fractures, caves, and pores is of great significance in the search for reservoir sweet spots and the prediction of reservoir productivity. With the advancement of exploration and development technology, the targets of oil and gas exploration move to deep high temperature, high pressure (HPHT) formations drilled with oil-based mud systems. The existing fracture evaluation methods often rely on dipole acoustic logging, electrical or acoustic formation micro-imaging, which utilize the difference of rock and pore fluid petrophysical properties for fracture detection, but the adverse HPHT conditions are a huge challenge to evaluate reservoir structure by such means. The tracer imaging technology (TIT) which utilizes pulsed neutron technology and tagged proppant containing high absorption cross-section element has been proposed for crack evaluation after hydraulic fracturing, but a quantitative evaluation of crack parameters, due to their low sensitivity caused by neutron self-shielding, has not been feasible. In this paper, the combination of the new pulsed neutron tool with multi-detector array design and oil-based mud with high absorption cross-section element is used to achieve the crack parameter evaluation in carbonate reservoirs under oil-based mud invasion condition via tracer element imaging. The special oil-based mud is injected into the carbonate formation through the borehole to enhance the difference of the nuclear properties between crack and rock. A multi-detector array tool that contains four gamma detectors arranged in a ring with 90 degrees between detectors is adopted to acquire capture the gamma spectrum in different orientations. Here, a new crack inversion method adopting a joint of the multi-element characteristic peak is used to eliminate the influence of neutron self-shielding to improve the response sensitivity of crack and calculation accuracy. The new method is suitable for all pore fluid types. Meanwhile, the effect of formation backgrounds which consist of formation matrix, pore fluid, and borehole fluid on the quantitative evaluation is analyzed and discussed for limitations of this method. To improve the recognition accuracy of the parameters in the image, the digital imaging recognition method based on artificial intelligence is applied in crack imaging for the information extraction of crack orientation. The effect of formation background on the quantitative evaluation of crack parameters is analyzed and discussed. Quantitative evaluation of carbonate with fractures, caves, and cavities can be realized with the new tracer imaging technology, which eliminates the saturation effect caused by neutron self-shielding to improve the calculation precision of fracture width. Finally, an example of carbonate formation with multiple cracks and formation background is simulated utilizing a Monte Carlo N-Particle transport model (MCNP). The calculation results of the crack density and crack width are presented and the crack orientation is determined from crack imaging, which is consistent with the model set. The result verifies the feasibility of the method.
Abstract The gas present in the Valhall overburden crest area interferes with the seismic data and obscures the fault detection (minor faults). Spatially resolving fractures and fracture network is essential for subsurface understanding and future well placement in this field, and it is a critical input to the dynamic reservoir model. Additionally, mapping the fracture network in poor permeable reservoir formation beyond the wellbore is crucial to identify completion intervals to maximize productivity/injectivity, and hence field value. The well 2/8-F-18 A was drilled on the crest of the Valhall field as a pilot water injector in Lower Hod formation, where core and data analysis formed the foundation for a future potential 11 well development. The well is placed in the southern section of the Valhall crest, and no major faults or strong amplitude features were mapped out in the overburden via surface seismic before drilling. In this case study, an integrated workflow is proposed and tested within the reservoir formation to identify “sweet” (permeable and fractured) zones beyond the wellbore. This is achieved using borehole acoustic data combined with image and ultrasonic imaging to characterize fracture networks beyond the borehole wall. The sonic imaging workflow identifies reflection events from fractures and faults and provides the true dip, azimuth, and location in 3-dimensions. This data is complemented by nuclear magnetic resonance (NMR), dielectric and spectroscopy data to understand reservoir petrophysics. NMR-derived permeability has also been evaluated for identifying high permeable zone in this formation, which primarily focuses on intergranular permeability of the formation a few inches away from the borehole wall. Reservoir textural heterogeneity and fractures beyond the wellbore wall make this method difficult to estimate or enhance the effective permeability estimate. The baseline assumption for the NMR permeability estimation is also not valid in Hod formation; the Timur and SDR equation needs significant change to match core permeability. Hence, the primary aim is to identify a fracture network that will help support water injection and maximize hydrocarbons production through them. The goal is to establish a workflow from the learnings of this study, performed on the pilot well, validate its findings with the near-field data (core, imaging, and ultrasonic), and optimize it if needed (described in the methodology section). The developed workflow is then intended to be used to optimize the placement of future wells. The results achieved from the integrated workflow identified a key fault and mapped it approximately 23 meters away on each side of the borehole. It also captures acoustic anomalies (high amplitudes), validated based on near-field data, resulting from a fracture network potentially filled with hydrocarbons. The final results show the sub-seismic resolution of the fracture and fault network not visible on surface seismic due to the gas cloud above the reservoir and frequency effect on the surface seismic when compared to borehole sonic data. Evidently enhancing the blurred surface image, which helps enhance the structural and dynamic model of the reservoir.
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.
ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development