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Wang, Haifeng (Schlumberger) | Thiel, Michael (Schlumberger) | Denichou, Jean-Michel (Schlumberger) | Salim, Diogo (Schlumberger) | Leveque, Soazig (Schlumberger) | Wibowo, Vera Krissetiawati (Schlumberger) | Woods, Chris (Woodside Energy Ltd.) | Baker, Darren (Woodside Energy Ltd.)
Abstract Recently the drilling industry has seen many advances in the application of deep directional electromagnetic (EM) measurements for mapping deeper into the reservoir, with the latest one capable of seeing over 250 ft above and below the wellbore providing unprecedented understanding of the reservoir. This measurement technology is now being used to look ahead of bit while drilling, for exploration wells to reduce drilling risks associated with unexpectedly penetrating certain formation. With the increasing complexity of the reservoirs that the industry is targeting, there is more and more quest for expanding the reservoir mapping capability, not just a 1D approach that can only map resistive boundaries on the vertical axis or near vertical axis and assume infinite extend in all other directions, but to enable geoscientists to better steer the well and better understand the reservoir structure and fluid contact in a full three-dimensional context around the wellbore. In this communication, the authors introduce a new solution to this quest for full three-dimensional real-time reservoir mapping. The solution is composed of three parts: a set of new measurements acquired downhole and transmitted to surface in real-time, a new inversion algorithm that is model independent and therefore fit for any reservoir complexity, and a new computing paradigm that make it possible to provide answers in real-time while drilling. The new set of measurements almost doubles the number of well logs that were acquired before and greatly enriches formations evaluation around the wellbore. The new algorithm, different from all previous algorithms, is not confined to any specific forms of models, making it suitable for exploring and finding solutions in complex reservoir settings. Finally, taking advantage of the latest advances in the Cloud computing, turnaround time of the new inversion is improved by over hundred times, thanks to the scalability of the algorithm design and Cloud computing infrastructure. Combining all these together allows to achieve three-dimensional reservoir map, without having to tradeoff between high resolution and depth of investigation. The 3D reservoir map that is generated from multiple transverse 2D inversion slices in real-time, enables timely update of reservoir model as drilling progress for the operator to make informed decisions. This new technology is currently deployed in several locations around the world and in different environments. In this paper, the authors review deployment results, to illustrate the technology, from preparation to real-time execution, and finally to post-job model update. With the ability of mapping in all directions while drilling, this technology opens the door to many applications and will enable the operators to target more complex reservoirs and achieving better geosteering results where 3D mapping and steering are required. In addition to its benefits for real-time operations, the technology also enables the geoscientists to update and calibrate their reservoir models with fine and accurate details, which can further benefit multiple disciplines including drilling, completion, production and reservoir management.
Abstract Siliciclastic turbidite lobes and channels are known to exhibit varying degrees of architectural complexity. Understanding the elements that contribute to this complexity is the key to optimizing drilling targets, completions designs and long-term production. Several methods for 3D reservoir modelling based on seismic and electromagnetic (EM) data are available that are often complemented with outcrop, core and well log data studies. This paper explores an ultra-deep 3D EM inversion process during real-time drilling and how it can enhance the reservoir understanding beyond the existing approaches. The new generation of ultra-deep triaxial EM logging tools provide full-tensor, multi-component data with large depths of detection, allowing a range of geophysical inversion processing techniques to be implemented. A Gauss-Newton-based 3D inversion using semi-structured meshing was adapted to support real-time inversion of ultra-deep EM data while drilling. This 3D processing methodology provides more accurate imaging of the 3D architectural elements of the reservoir compared to earlier independent up-down, right-left imaging using 1D and 2D processing methods. This technology was trialed in multiple wells in the Heimdal Formation, a siliciclastic Palaeocene reservoir in the North Sea. The Heimdal Fm. sandstones are generally considered to be of excellent reservoir quality, deposited through many turbiditic pulses of variable energy. The presence of thin intra-reservoir shales, fine-grained sands, heterolithic zones and calcite-cemented intervals add architectural complexity to the reservoir and subsequently impacts the fluid flow within the sands. These features are responsible for heterogeneities that create tortuosity in the reservoir. When combined with more than a decade of production, they have caused significant localized movement of oil-water and gas-oil contacts. Ultra-deep 3D EM measurements have sensitivity to both rock and fluid properties within the EM field volume. They can, therefore, be applied to mapping both the internal reservoir structure and the oil-water contacts in the field. The enhanced imaging provided by the 3D inversion technology has allowed the interpretation of what appears to be laterally stacked turbidite channel fill deposits within a cross-axial amalgamated reservoir section. Accurate imaging of these elements has provided strong evidence of this depositional mechanism for the first time and added structural control in an area with little or no seismic signal.
Abstract Cased-hole acoustic-wave modeling using the slip-interface theory is applied to cement bond evaluation, allowing for characterizing various bonding issues caused by poor bonding, lack of a cement, interface roughness and irregularity, micro-annulus, etc. The new theory models the interface between casing and cement (or cement and formation) as a slip boundary governed by normal and tangential coupling stiffness parameters. With the new theory and the stiffness parameters, we can model various wave phenomena for bond quality variation between the free-pipe and well bonded conditions. The modeling shows that wave amplitude variation is primarily controlled by the tangential (or shear) coupling stiffness, providing the theoretical foundation for developing an inversion procedure to estimate this parameter from field acoustic logging data. In the inversion procedure, the maximum stiffness value is first determined by matching the modeled and measured waveform data for the well bonded condition. Using the stiffness value as a reference, the stiffness values for the borehole section of interest are inverted by minimizing the modeled and measured waveform data, resulting in a continuous coupling stiffness curve to characterize the cement bond quality of the borehole section of interest. Because the stiffness parameter is directly related to the cement bond strength, the new stiffness-based method is advantageous over the existing wave-amplitude-based method and can thus better characterize and quantify the cement bond quality.
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 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.
Skelt, Christopher (Chevron Energy Technology Company) | TenCate, James (Los Alamos National Laboratory) | Guyer, Robert (University of Massachusetts) | Johnson, Paul (Los Alamos National Laboratory) | Larmat, Carène (Los Alamos National Laboratory) | Le Bas, Pierre-Yves (Los Alamos National Laboratory) | Nihei, Kurt (Chevron Energy Technology Company) | Vu, Cung (Chevron Energy Technology Company)
We present experimental and modeling results and a downhole logging tool concept resulting from a research collaboration between Chevron Energy Technology Company and Los Alamos National Laboratory investigating using nonlinear acoustics applications for natural fracture characterization and assessing near-wellbore mechanical integrity or drilling-induced damage. The generation of a scattered wave by noncollinear mixing of two acoustic plane waves in an acoustically nonlinear medium was first documented several decades ago. If the frequency ratio and convergence angle of the two waves and the compressional-to-shear velocity ratio of the medium where they intersect meet certain conditions, their interaction creates a scattered third wave, propagating in a predictable direction, with a frequency equal to the sum or difference between the two primary wave frequencies and an amplitude dependent on the nonlinearity at the intersection location. The conditions resulting in this scattering and the properties of the scattered wave are thus governed by the physics of the interaction, resulting in a set of “selection rules” that are the key to the measurement principle introduced here. If the two transmitted plane waves are oriented such that the third wave returns to the borehole, the phenomenon may be used as the basis for a logging tool measuring acoustic nonlinearity around the wellbore circumference, with a secondary measurement of the compressional-to-shear velocity ratio. Laboratory measurements supported by finite-difference and analytical modeling confirmed that the mixing of two plane compressional waves generated a shear wave as predicted by the selection rules in a large Berea sandstone block, confirming the potential for a downhole tool with a depth of investigation in the range 15 to 20 cm. Historical data show that nonlinearity in core samples is primarily caused by a lack of mechanical integrity. In the oil field, this may be microfractures in tight rock unconventional reservoirs or incipient near-wellbore failure while drilling. This prompts applications to fracture characterization and calibration of mechanical earth models. The main practical challenge for a downhole logging tool is injecting powerful directional acoustic energy into the formation. We envisage an openhole tool making sequential station measurements using transmitters built into hydraulically controlled pads contacting the borehole wall. Noncollinear mixing may be activated by maintaining the frequency of one transmitter constant while sweeping the other through the range of frequency ratios predicted by the selection rules, resulting in a received sum or difference frequency signal that rises to a peak and then falls. Alternatively, the frequency ratio may be maintained while steering one of the acoustic beams. The peak signal amplitude indicates the coefficient of nonlinearity, which is sensitive to lack of mechanical integrity caused by natural fractures or mechanical disaggregation. The frequency ratio at which it occurs is an indicator of the shear-to-compressional velocity at the location where the two beams cross. In this manner, a record of nonlinearity along or around the borehole can be envisaged. The physics of acoustic nonlinearity is well established, and our laboratory measurements have determined that the phenomenon of interest should occur and be measurable in the subsurface. Overcoming the engineering challenges would bring new formation evaluation insights unique to this measurement principle.
Abstract Sonic data are commonly acquired in exploration, appraisal, and development wells using wireline, logging-while-drilling, or through-the-bit conveyance for applications within petrophysics, geophysics, geomechanics, and geology disciplines. The measurement data require processing to obtain elastic wave slownesses (inverse of velocity) and associated attributes before the results can be used in petrotechnical workflows. The objective of the digital transformation is to streamline and automate the processing workflow to reduce user intervention and turnaround time while increasing the accuracy of results and possibly extracting more answers by fully utilizing all waveform attributes, which consequently benefits downstream applications. There are four workflows that are the foundation of the transformation. They support the overall goals of reducing user interactions and providing robust results in a timely manner for continuous slowness logs. First, data-driven inversions done during acquisition with automatic quality control and interpretation flags immediately provide assurance about the data quality and identify formation intervals that require further evaluation. Second, automatic dipole-flexural shear extraction is done using physics-based machine learning (ML) where purely data-driven models are inadequate due to borehole or geological conditions. The physics-based ML utilizes cloud-based computing that is needed for large volume synthetic data generation and neural network training. Third, a multiresolution analysis of the monopole waves for the compressional slowness uses automatic peak detection on multiple receiver levels removing any subjective manual labeling after the semblance processing. Finally, the multimode (flexural and Stoneley) inversion determines anisotropic constants and accounts for mud-speed variations in the borehole, including detailed uncertainties. The new methods address underlying concerns most users and waveform processing experts already observe in their sonic deliverables. Enabling wellsite algorithms to be more automatic and data driven improves the robustness of the field deliverables and provides insight into the quality of the data. For the shortcomings with regards to borehole or geological conditions such as laminations, sharp lithological transitions, or the presence of anisotropy, the physics-based ML is shown to honor the physics of the dipole flexural mode, while the multiresolution for the monopole provides physics-based reasoning for discrepancies between the geological layering and receiver aperture. By incorporating the range of results derived from the inversions with advanced interpretations such as transversely isotropic constants, these uncertainties can be further used in stochastic models in downstream workflows. All these methods are fully automated and can be done in a short timeframe to be used without doubt in operations.
Dan, Lingling (CNOOC Energy Technology-Drilling&Production Company) | Shi, Changlin (CNOOC Energy Technology-Drilling&Production Company) | Wen, Jiatao (China National Offshore Oil Corporation Limited) | Hu, Yunting (CNOOC Energy Technology-Drilling&Production Company) | Wei, Li (CNOOC Energy Technology-Drilling&Production Company) | Li, Yunxiu (CNOOC Energy Technology-Drilling&Production Company) | Zhang, Jian (CNOOC Energy Technology-Drilling&Production Company) | Tian, Panpan (CNOOC Energy Technology-Drilling&Production Company)
Abstract G oilfield in China is a dual medium oilfield dominated by carbonate reservoir, where faults and fractures are well developed. After G oilfield was put into production, water cut has rised quickly and oil production decreased rapidly. The heterogeneity of fracture development is very strong. Because of unclear understanding of the fracture distribution, oilfield development plan can not accurately identify risks and potentials accurately. As a result, production goals are not as expected. Due to limited core data and imaging logging data, conventional methods cannot characterize fracture information in this oilfield accurately and comprehensively. In order to solve the problems mentioned above, this paper makes full use of logging, geology, seismic and other data to carry out fine characterization of fracture with multi-information fusion fracture modeling based on neural network. Firstly, neural network technology is used to predict the fracture density curves of wells with conventional logging data. Secondly, combined the nonlinear fusion of multiple pre-stack seismic attributes including Tectonic Stress Field, P-Wave AVO and Seismic Discontinuity Detection based on neural network, the fracture density probability model is created to predict three-dimensional distribution of fractures. Thirdly, the fracture Intensity model is set up under the constraint of strict variation function analysis and fracture density probability model. Finally, fracture parameters are obtained according to core observation and imaging logging data, and the fracture network is established by geostatistics modeling method and coarsened equivalent into fracture property model. Blind well validation and dynamic validation are used to verify the validity of fracture model. Under the guidance of research results in this paper, the oilfield development plan is optimized and adjusted. It has also been proved that fracture characteristic of new wells is consistent with pre-drilling prediction result. Most areas with relatively high fracture development degree are corresponding to well groups with faster water cut rising rate. Against the backdrop of global oil economic downturn, fracture modeling technology studied in this paper provides strong technical guidance for oilfield development plan, therefore reducing oilfield development risks and maximizing economic benefits.