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Wang, Gong Li (Schlumberger) | Ito, Koji (Schlumberger) | Hong, Xiaobo (Schlumberger) | Salehi, Mohammad Taghi (Schlumberger) | Shi, ZhanGuo (Schlumberger) | Allen, David (Schlumberger) | Rabinovich, Michael (BP) | Meyer, Jeffrey (Repsol)
Triaxial induction is a powerful tool to identify thin-bed reservoirs that would be missed with conventional techniques and to improve the accuracy of net pay estimation. On the other hand, it has been noticed that with the standard processing, the resolution of horizontal and vertical resistivities cannot match that of the 2-foot array induction resistivity. Sometimes large apparent anisotropy can be seen in high-resistivity clean sands, which has caused confusion in data interpretation and reserve evaluation.
Researchers have long known that triaxial induction data contains high-frequency information from the formation through the abrupt change and spikes of transverse coupling logs near bed boundaries. In this paper, we present a novel pixel inversion equipped with a revamped cost function and a data-driven regularization scheme to better resolve thin beds by using the high-frequency information that had not been used to its full potential.
The pixel inversion is a variant of the maximum entropy inversion that has proved to be superior for conventional induction data. However, the direct use of the method for triaxial induction data tends to give a slowly varying vertical resistivity log that fails to resolve thin beds as desired. This issue is resolved by means of two adaptive relaxation terms for the smoothness regularization determined by utilizing the data sensitivity to horizontal and vertical resistivities that evolve continuously with the iteration.
In favorable conditions, results on a variety of synthetic models show that a thin bed of less than 1 ft can be detected with deep arrays. In contrast, a bed less than 2 ft can hardly be seen with previous inversions. Results also show that apparent anisotropy ratio is reduced significantly in high-resistivity isotropic cases emulating clean sands. Moreover, the horizontal and vertical resistivities compare favorably with array induction logs in terms of accuracy in the clean sand cases.
Field cases confirm that the pixel inversion is clearly superior to the standard triaxial 1D inversion as far as the vertical (or along-hole) resolution is concerned. The thin beds that can now been seen on horizontal and vertical resistivity logs of the pixel inversion are in good agreement with nuclear logs. In the cases of high-resistivity clean sands, the large apparent anisotropy is largely eliminated with the pixel inversion.
Vo, Hai (Chevron Energy Technology Company) | Mallison, Brad (Chevron Energy Technology Company) | Kamath, Jairam (Chevron Energy Technology Company) | Hui, Robin (Chevron Energy Technology Company) | Dufour, Gaelle (Chevron Energy Technology Company)
Accurate evaluation of recovery mechanisms in fractured reservoirs is challenging due to the large permeability contrast at the matrix-fracture interface. Dual Porosity-Dual Permeability (DPDK) models are typically used in field-scale simulations but can be biased by their use of idealized fracture networks and matrix-fracture interactions. Unstructured Discrete Fracture Models (USDFMs) are able to capture the complex physics accurately but can be computationally demanding. Embedded Discrete Fracture Models (EDFMs) integrate discrete fracture networks with a structured matrix grid and are the focus of this study.
Our study considers dense and sparse fracture networks extracted from a field-scale fracture carbonate reservoir model. EDFMs are constructed for different matrix grid resolutions, and simulations are performed to evaluate gravity drainage, spontaneous imbibition, viscous displacement. In each case, EDFM results are compared with highly refined USDFM reference solutions and equivalent DPDK simulations.
We improve the EDFM single phase matrix-fracture transfer function to account for pseudo-steady state and fracture interactions. In the cases of gravity drainage, EDFM simulations converge to the fine scale reference solutions with matrix grid refinement. For the coarser grids, the new matrix fracture function gives much better results than the ones reported in the literature. For spontaneous imbibition, both EDFM and USDFM overpredict the rate of spontaneous imbibition with coarse matrix grids, but the overestimation is less severe than with DPDK. In viscous displacements, EDFM overestimates recovery with coarse grids and displacement efficiency diminishes with refinement. DPDK underpredicts recovery from viscous displacement at all resolutions.
Challenges remain in the upscaling and flow simulation of reservoir models from strong heterogeneity that may arise when representing complex patterns of connectivity and barriers. This is especially true in high contrast systems, e.g. for carbonate reservoirs, where statistical upgridding and upscaling approaches developed for clastic reservoirs perform less well. This has led to the development of a novel "Distance Based" upgridding technique which we combine with "Diffuse Source" upscaling to successfully simulate such models.
We replace previously developed variance-based statistical sequential layer grouping reservoir coarsening analyses, with a novel distance-based calculation. It relies on the local errors in the interstitial velocity and the time-of-flight introduced when layers are grouped, as a measure of distance between reservoir models. The use of a distance measure allows for the inclusion of flow capacity in this calculation, and avoids the strong biases that arise from the previous variance-based approaches, especially with high contrast systems.
We utilize the "Diffuse Source" (DS) upscaling approach to obtain the intercell transmissibility and well indices for the upscaled reservoir model. The DS approach is an extension of pseudo-steady-state (PSS) flow-based upscaling that utilizes the diffusive time of flight to identify well-connected sub-volumes in each adjacent pair of coarsened reservoir grid cells. DS upscaling retains the same localization advantage as the PSS approach. Unlike steady state upscaling, there is no coupling to a global flow field and local-global iterations are not required. DS upscaling was previously developed for more general reservoir coarsening, but the description now includes an extremely simple implementation for 1×1×N layer coarsening that naturally avoids the issues that arise in the use of the harmonic average for the vertical transmissibility.
Starting from a high-resolution fine scale 3D geologic model, sequential layer grouping provides us with a series of increasingly coarsened reservoir models. Each model in turn minimizes the integrated distance between the fine and coarsened models, which is used as a measure of heterogeneity lost during coarsening. From these layer designs we apply a combined cost and heterogeneity objective function to determine an optimal layer design, which is then used for upscaling and flow simulation.
We show that this distance-based optimal layer design does not experience the over-grouping of layers that arose from the previous variance-based approach. The new approach has been able to integrate the flow capacity similar to a Lorenz plot into the calculation of distance between reservoir models to include the impact of reservoir quality. This replaces the simple use of a net-to-gross cutoff utilized in previous work. The distance calculation uses a hyper-volume Lebesgue measure which provides a consistent means of combining different physical attributes: in this case interstitial velocity and the time-of-flight. The generalization of the hyper-volume to multiple properties, e.g., anisotropic permeability, is straight-forward.
1×1xN layer grouping is examined specifically to show the improvement in the optimal layer design compared to the previous statistical analyses. Areal coarsening is increased beyond 1×1 to show the general applicability of the DS upscaling approach.
The combination of distance-based upgridding and DS upscaling is tested and performs extremely well on a series of sector, outcrop and full-field 3D reservoir models using a research simulation code and a commercial finite difference simulator. These models include the SPE10 reference model, the Amellago carbonate model, and additional full field examples.
A novel distance-based measure of reservoir heterogeneity has been developed and applied to the design of an optimal reservoir simulation layering scheme, given a prior 3D geologic model. The distance measure combines elements of the Lorenz plot and previous variance-based analyses to avoid the strong biases and collapse of layering seen in the earlier approaches. Flow simulation based on Diffuse Source upscaled properties are shown to perform extremely well compared to the fine scale model.
Digital Rock Physics (DRP) serves as a powerful computational tool for analyzing the petrophysical properties of rock. Obtaining the properties of a fine-grained sample, such as shale is very challenging due to its highly variable and complex nature. Capturing the micro-features of this structure requires advanced microscopy techniques such as SEM (scanning electron microscopy) and FIB-SEM (Focused ion beam- scanning electron microscopy). However, performing advanced microscopy techniques to capture the heterogeneity of the sample is quite difficult; the slow speeds of data collection and analysis are two critical problems that limit more extensive use of this technology. In this study, an alternative approach is proposed to quantify the physical properties of the rock sample. This study aims to accelerate the process of SEM image analysis and reduce the computational cost by using machine learning. A deep learning-based method, Convolutional Neural Networks (CNN), is utilized to predict the properties from the 2D grayscale SEM images of Marcellus shale. The image data set is segmented by applying watershed segmentation to extract the pore network of the sample. Porosity and average pore size are the two properties computed for this study. The SEM images are down sampled to low-resolution images which are fed as an input, and the computed properties are used for training and validation of the CNN network. A detailed description of the image segmentation process, CNN architecture and the predicted results are discussed in this work.
Allen, David (University of Texas at Austin) | Stokes, Shannon (University of Texas at Austin) | Tullos, Erin (ExxonMobil Upstream Research Company) | Smith, Brendan (SeekOps) | Herndon, Scott (Aerodyne Research, Inc.) | Dewitt, Langley (AECOM) | Flowers, Bradley (AECOM)
Governments in the U.S, Canada, Europe and other countries have promulgated methane mitigation regulations, and many companies have undertaken voluntary programs beyond regulatory requirements. As programs expand, emerging technology developers have responded with the development of multiple monitoring, detection and quantification systems for methane emissions. Methane detection and quantification systems operate at multiple spatial and temporal scales and when these methods are compared against each other and against inventories of methane emissions, analyses must carefully match spatial and temporal scales. This field trial compares multiple methane detection and quantification methods and compares the measurements to engineering estimates of emissions. Engineering estimates of the detailed temporal patterns of methane emissions at oil and gas production sites reveals the challenges of comparing measurements of individual sources that are not exactly synchronized. Because some detection and quantification methods can interfere with one another, most inter-comparisons are based on asynchronous measurements and approaches for inter-comparing ensembles of measuements are suggested.
Knowledge of pore pressure, in-situ stress, and lithology in unconventional reservoirs is important for safe and economic drilling, hydrocarbon production, and geomechanics applications such as wellbore stability analysis and hydraulic fracturing. Reliable predrill predictions of pore pressure, in-situ stress, and lithology are thus required for safe drilling and optimal development in such reservoirs. In the Permian Basin, changes in lithology occur over vertical depths that cannot be resolved by seismic velocities obtained by kinematic analysis, as these have poor vertical resolution. To obtain improved vertical resolution, seismic prestack depth-migrated (PSDM) data are input to amplitude variation with offset (AVO) inversion, for an area in the Delaware Basin where wide-offset 3D seismic data are available. AVO inversion provides estimates of both P- and S-impedance. The results are used to build a 3D mechanical earth model, which is employed to predict pore pressure, in-situ stress, and geomechanical properties. The model enables integrating the results of seismic inversion with drilling data, measurements on cores, wireline logs, formation and fracture closure pressures, and other data. By employing P- and S-impedance, and their ratio, pore pressure, in-situ stress, and lithology derived from seismic prestack inversion provides greater resolution than estimates obtained using seismic velocities from kinematic analysis. Examples from the Permian Basin illustrate the importance of the results for unconventional reservoir development.
Smooth solid surfaces of reservoir rocks are assumed in formation evaluation, such as NMR petrophysics and reservoir wettability characterization through contact angle measurements. Measuring the degree of surface roughness, or smoothness, and evaluating its effects on formation evaluation are topics of research. In this paper, we will primarily focus on details in characterizing solid surface roughness, though its applications will also be exemplified.
Surface roughness can be measured by methods of contacts and non-contacts techniques, such as stylus profilometer, atomic force microscopy, and different kinds of optical measurements. Each technique has different sensitivities, measurement artifacts, resolutions and sizes of field-of-view (FOV). Intuitively, while a finer resolution measurement provides the closest account of all surface details, the corresponding small FOV may compromise the representativeness of the measurement, which is particularly challenging for charactering heterogeneous samples such as carbonates. To balance the FOV and measurement representativeness, and to minimize artifacts, laser scanner confocal microscopy (LSCM) is selected in this study.
In developing a methodology for quantifying the surface roughness (
Results for the more than 20 rock samples tested indicate that rocks with similar rock types have similar
The second half of the paper is devoted to the use of
This session will set the stage for what we can tell today between wells and what we want to be able to do in the future. The group will brainstorm at least two circumstances to initially attempt to determine the state of industry and identify topics for closing gaps in what we can know today. The group will frame our understanding in technical and commercial terms to highlight choices to be made, potential shortcomings, and aspects in regards to perfection and steps to potentially get there. The initial brainstorm will be blended topically into the remaining agenda as an initiation point of discussion. The information obtained from many oilfield measurements fall at the ends of a spectrum – as they are either obtained by probing or imaging the near-wellbore region at high vertical resolution or they illuminate large reservoir volumes at poor vertical resolution; and may be more sensitive to rock properties than to fluid behavior in the reservoir.