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Interpretation of sonic data acquired by logging-while-drilling tool or wireline tool in cased holes is complicated by the presence of drill pipe or casing because those steel pipes can act as a strong waveguide. Traditional solutions, which rely on using frequency bandpass filter or waveform arrival-time separation to filter out the unwanted pipe mode, often fail when formation and pipe signals co-exist in the same frequency band or arrival-time range. We hence develop a physics-driven machine learning-based method to overcome the challenge. In this method, two synthetic databases are generated from a general root-finding mode-search routine based on two assumed models, one is defined as a cemented cased hole for wireline scenario, another with a steel pipe immersed in a fluid-filled borehole for the logging-while-drilling scenario. The synthetic databases are used to train neural network models, which are first used to perform global sensitivity analysis on all relevant model parameters so that the influence of each parameter on the dipole dispersion data can be well understood. A least-squares inversion scheme utilizing the trained model was developed and tested on synthetic cases. The scheme showed good results and a reasonable uncertainty estimate was made for each parameter. We then extend the application of the trained model to develop a method for automated labeling and extraction of the dipole flexural dispersion mode from other disturbances. The method combines the clustering technique with the neural network model-based inversion and an adaptive filter. Testing on field data demonstrates that the new method is superior to traditional methods because it introduces a mechanism from which unwanted pipe mode can be physically filtered out.
This novel physics-driven machine learning-based method improved the interpretation of sonic dipole dispersion data to cope with the challenge brought by the existence of steel pipes. Unlike data-driven machine learning methods, it can provide global service with just one-time offline training. Compared with traditional methods, the new method is more accurate and reliable because the processing is confined by physical laws. This method is less dependent on input parameters; hence a fully automated solution could be achieved.
Deep directional resistivity LWD measurements have been shown to be sensitive to resistive transitions over a broad range of distances around the tool from tens to hundreds of feet. These detected transitional surfaces are primarily used to detect formation resistivity boundaries and assist with mapping geological profiles. The inverted formation dip and vertical resistivities are also resolved in the same search space. While the formation dip is used in conjunction with the reservoir-mapping interpretation results, the vertical resistivity, specifically the vertical and horizontal resistivity ratio, or anisotropy, has not received the same amount of attention.
Resistivity anisotropy is useful when calculating the formation resistivity in layered formations, as conventional resistivity tools measure the resistivity in one direction, which is perpendicular to the tool axis. With conventional induction and propagation resistivity tools, the electrical current preferentially transits the conductive lithologies, resulting in an apparent resistivity measurement that does not represent the true sand resistivity. The petrophysical evaluation often results in an apparent high-water saturation, which can result in incorrect decisions to abandon a prospect.
To understand two new fields located onshore Alaska, three horizontal appraisal wells were drilled with deep directional resistivity LWD technology. While the primary goal was to characterize the lateral resistivity profile and bed boundaries away from the wellbore, accurate water saturation calculations along the horizontal section are critical for making appropriate development decisions.
A review on how and why deep directional resistivity LWD technology is sensitive to anisotropy and how anisotropy is derived from parametric inversions is presented with a comparison between deep directional resistivity LWD measurements, 3D petrophysical modeling of propagation, and offset well triaxial induction anisotropy measurements. Integrating 3D petrophysical processing and triaxial-induction technology into deep directional resistivity LWD measurements add to the strength of the anisotropy output. The comparison shows that deep directional resistivity LWD measurements can be used independently to give accurate anisotropy results.
The result of this process provides a corrected resistivity measurement of vertical and horizontal resistivity in anisotropic formations for petrophysical models. Use of the corrected resistivity as a true resistivity (Rt) input for water saturation will ultimately drive better development decisions.
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.
Al Khalifa, Nasser (KOC) | Hassan, Mohammed (KOC) | Joshi, Deepak (KOC) | Tiwary, Asheshwar (KOC) | Al Shammari, Yousef Suhail (KOC) | Clegg, Nigel (Halliburton) | Clarion, Benjamin (Halliburton) | Kharitonov, Alexander (Halliburton) | Pan, Li (Halliburton)
Through decades of production and water injection, Umm Gudair reservoir fluid distribution have changed significantly, resulting in an increase of uncertainties on fluid levels and subsequent water cuts in production. Different well architectures have been implemented pilot holes, deviated wells or horizontals, but the development of such mature fields comes with inherent difficulties, as offset data does not necessarily reveal the current reservoir properties and fluid contact position. Frequently, costly and time-consuming additional operations such as cement plugs or sidetracks are required to resolve an unforeseen water saturation of the reservoir. However, these methods have a limited efficiency in reducing the water percentage over the time of well production.
In this challenging environment, the Umm Gudair asset has implemented a different approach to well construction built upon the combination of an ultra deep resistivity tool with a previously unattempted benchmarking scenario for a look around inversion. Drilling a trajectory of 45° inclination in order to proactively identify the oil water contact (OWC) in the far field below, and confirm this forecast with an actual resistivity measurement during its penetration. This unprecedented process shows great opportunities in optimizing future well placement and production performances. The main inputs in this success come directly from the capability of the inversion of the electromagnetic measurements in various drilling conditions, as well as a thorough preparation and collaboration between the operator and the service company.
Before implementing this technology, it is critical to assess the expected performance by understanding the different parameters which affect the performance of the tool. The study of the different offsets gave an overview of potential resistivity contrast between fluids and their contact positions. The pre-well study is therefore essential to optimize depth of detection (DOD) versus sensitivity through forward modeling of various frequencies and spacing selections. This phase is also necessary for the team to understand what can be expected from the service with the elaboration of different scenarios based on theoretical tool responses and communication protocol.
This case study shows how an innovative scenario and collaboration between operator and service company reveals a new capability to place a well drilled at mid-angle in the lowest water saturated part of the reservoir using inverted resistivity measurements. The economic benefit and post job analysis conducted post well confirm the promising outlooks of utilizing an ultra-deep resistivity service in a mature field environment.
Deep directional resistivity logging-while-drilling technology and its measurement processing are instrumental in strategic well placement, landing, geosteering, and reservoir understanding. However, since the measurements are truly at the reservoir scale, reservoir complexities limit the applicability of 1D and sometimes even 2D interpretation models and inversions. A new full-3D imaging inversion is introduced to accurately map reservoir sections where 1D and 2D models are not adequate to represent the reservoir. The imaging inversion uses a 3D EM simulator and accounts for both resistivity and resistivity anisotropy and is fully consistent with the 1D and 2D inversion results in sections of lower complexity. The inversion is validated with a realistic 3D reservoir model and applied to field datasets from the North Sea, revealing complex 3D detail consistent with all the data.
Azimuthal gamma ray (GR) logging-while-drilling (LWD) tools have demonstrated great value for geosteering applications in directional drilling. Their ability to indicate the relative stratigraphic position of the drilling assembly can determine whether the well should be steered up or steered down to stay in the target zone. However, this determination remains rather qualitative and largely depends on the user experience, especially when the quality and amount of real-time data is limited by intrinsic statistical noise, telemetry bandwidth, rate of penetration (ROP), and other drilling conditions. In addition, the commonly used geosteering modeling for azimuthal GR is geometry based only, without considering any measurement physics. Thus, a new forward-modeling and inversion method has been developed to provide an optimized pre-job planning and potentially quantitative real-time decision-making for more accurate geosteering with azimuthal GR.
The geosteering question can be simplified mathematically to a prediction of separation between azimuthal GR curves when approaching or passing a bed boundary in a two-bed formation model. Separation will give an indication of a steering direction change, even in the simplest case of only up- and down-facing GR curves. In this study, a method was developed to solve this question in seconds with only two factors: measurement precision and front-to-back ratio. The theoretical up- and down-facing readings of an azimuthal GR tool can be forward-modeled accurately from this ratio for any bed boundary changes. Combined with measurement precision, the counting statistics effects can be added to the model to mimic the real-world log curves, and this for any pre-selected stratigraphic marker.
The forward model results agree well with industrial standards of full Monte Carlo nuclear simulation and its deterministic nature allow it to run very fast. Thus, various scenarios can be evaluated quickly during either the pre-well phase or the operation. Detection limits achieved by any azimuthal GR tool in any given scenario can be statistically predicted for various confidence levels (e.g., 95% possibility of up/down curve separation). Thus, based on the detection limits, confidence level, and their variation with ROP, .etc. the drilling and geosteering plan can be optimized to reach the best ROP confidently without compromising the steering capability. Also in real time, when a potential separation of up- and down-facing azimuthal GR curves appear on the log, inversion of this model can be carried out to derive the possibility that this separation truly reflects formation changes to offer some quantitative insight to make steering decisions. Inversion of the modeling also has the potential to help recover the true API values of the formation beds and enhance the detection of the bed boundary positions.
The novelty of this approach stems from the statistical nature of nuclear counting statistics and the derivation of front-to-back ratio. Front-to-back ratio, when properly defined, is a factor that fully represents the measurement physics of the tool. In addition to the aforementioned applications, an overall coverage chart can be recalculated as a quick look-up reference to measure the effectiveness of azimuthal GR. The chart reflects the detection limits that an azimuthal GR tool can resolve for geosteering in a 0–200-API sampling space at a certain confidence level. Overall, the paper includes a detailed description of the model and its inversion, applications, and example log demonstrations from early trials.
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.
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.