The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Abstract Ultra-Deep electromagnetic (EM) azimuthal measurements provide critical data for well placement operations, allowing real-time assessment of resistivity boundaries over 100ft from the well. Historically, 1D and 2D inversions displayed vertical boundary changes, however they do not resolve azimuthal changes. Other 3D approaches lacked real-time aspect or endured costly deployment. This paper describes integration of real-time 3D EM Inversions for both inclination and azimuth trajectory corrections, to optimize well path and increase efficiency while drilling HA/HZ wells. Triaxial ultra-deep electromagnetic borehole logging tools provide 9 component multi-frequency data from multiple receiver assemblies, logging the 3D EM field around the wellbore. Although the raw component data shows observable signal changes representing the 3D EM field, evaluating this raw data in real-time is challenging. Therefore, a 3D EM inversion was implemented to provide real-time 3D representation of the geological structure and fluid distribution around the well. The 3D EM Inversion algorithm has been optimized to return model updates within a few minutes. The near real-time process allows well placement decisions to be made very quickly to help maintain the well path within the target reservoir. Real-time monitoring of the 3D EM inversion revealed a lateral disparity in the resistivity distribution for the target reservoir. In a particular interval, the presence of higher resistivity to the right-hand side of the well bore was revealed. The increase in resistivity was identified as improved reservoir properties. The trajectory of the well was adjusted to the right, interactively adjusting the plan. As with all deviations from the plan the impact of the azimuthal turn was assessed both in terms of safety and the potential impact on running the completion, no risks were identified, and a successful turn was conducted. Using the same methodology, a turn to the left of the well bore was conducted towards the toe of the well. Optimizing a wells TVD with inclination is common, but azimuthal changes based on LWD readings are much less so. The 3D Inversion and azimuthal resistivity measurements helped to minimize the loss of the effective length of the wellbore during the drilling in a complex geological structure. The effectiveness of the azimuthal turn can be assessed by comparing the resistivity of the actual and planned trajectories, estimated to have a 24-foot separation. The actual trajectory was placed in a zone with optimum quality reservoir without loss of the effective length (100% NTG). Real-time 3D Inversion has enabled for the first time the ability to steer azimuthally based on Ultra-deep EM data, changing the hole azimuth in real-time to target improved reservoir properties. The method of correcting the well path with azimuth as well as inclination in real-time based on 3D Inversion data ensures maximum efficiency for the well placement process in complex geology which can show vertical and azimuthal variations in resistivity. The depths of detection possible with Ultra-Deep EM tools allows these decisions to be made early reducing tortuosity of the well path while revealing the position of resistivity boundaries in all directions.
_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207385, “Ultradeep LWD Electromagnetic Directional Resistivity for Waterflood Mapping: A Game Changer,” by Parmanand Thakur, SPE, Maniesh Singh, SPE, and Saif Al Arfi, ADNOC, et al. The paper has not been peer reviewed. _ Abu Dhabi’s thick Lower Cretaceous carbonate reservoirs experience injection water overriding oil. The water is held above the oil by negative capillary pressure until a horizontal borehole placed at the reservoir base creates a small pressure drawdown. This causes the water above to slump unpredictably toward the horizontal producer, increasing water cut and eventually killing the well under natural lift after a moderate amount of oil production. Water slumping is difficult to forecast using the reservoir model. This paper showcases the successful deployment of an ultradeep electromagnetic (EM) directional resistivity instrument to map injection-water movement. Ultradeep Electromagnetic Technology Two of the most-significant drivers of the depth to which an EM field will penetrate the formation are the spacing between transmitter and receiver antennas and the transmission frequencies. These can be modified at the tool-design stage and with ultradeep EM tools by placing the transmitter and receiver antenna on different drill collars. Modifying the bottomhole assembly (BHA) allows the spacing between the antennas to be optimized for reservoirs of differing thickness (Fig. 1). Increasing the transmitter-receiver spacing, coupled with decreasing the transmission frequency, generates an EM field that penetrates further into the formation and allows a greater depth of investigation. The transmitter is placed closest to the bit, with two receivers placed farther back in the BHA. To map reservoir units close to the wellbore in greater detail, the first receiver can be connected directly to the transmitter. To increase depth of investigation in thicker reservoirs, the spacing can be increased. The farther the receivers are placed from the transmitter, the greater the depth of detection. Lower frequencies are used by these tools in the range of 2–64 kHz. In low- to mid-range resistivities, the lower frequencies provide a greater depth of investigation but with lower spatial resolution. At higher resistivities, greater depths of detection can be achieved with the higher-frequency options. In one field example, using the lowest frequencies and a transmitter-to-receiver spacing of 133 ft, it was possible to identify resistivity boundaries up to 225 ft away from the well. Measurements from ultradeep azimuthal EM tools are pulsed to the surface as components of the EM field from multiple spacings for multiple frequencies. Inversion algorithms are used to convert this to a model representing the subsurface, depicting resistivity boundaries. The position of these boundaries can be tracked many feet from the wellbore and used to place the well in the optimal position during drilling. This technology can be used to map units that the well may never penetrate, such as the water zone in this case, which was anticipated to be approximately 80 ft above the well.
Ooi, Guang An (King Abdullah University of Science and Technology) | Khater, Moutazbellah (King Abdullah University of Science and Technology) | Ozakin, Mehmet Burak (King Abdullah University of Science and Technology) | Mostafa, Tarek M. (King Abdullah University of Science and Technology) | Bagci, Hakan (King Abdullah University of Science and Technology) | Ahmed, Shehab (King Abdullah University of Science and Technology)
Abstract Casing integrity inspection tools are indispensable in identifying defects that threaten the structural integrity of oil wells. In particular, electromagnetics-based (EM-based) inspection tools are commonly used for multi-casing corrosion imaging. These tools measure the scattered EM fields inside the inspected casings and generate estimations of metal loss properties. However, the interpretation of EM measurements is difficult due to their intrinsic nonlinearity with respect to defect characteristics. In this paper, a new machine learning-based inspection framework is developed to generate accurate cross-sectional images of casings to characterize metal loss location and shape. A hybrid neural network (HNN) consisting of a main structure that integrates both convolutional and recurrent layers, as well as a parallel cross-frequency module with convolutional filters predicts the cross-sectional images of the inspected casings. Metal losses on the inner surface of the inspected casing, as well as fully-penetrating losses, are detected using high-frequency signals. On the other hand, low-frequency signals enable the detection of metal losses on the outer surface, in addition to the two previous kinds of losses. The resulting inspection scheme requires only four receiver (RX) coils for each frequency of signals to accurately predict both the azimuthal location and size of defects.
Abstract Combinations of dielectric and NMR measurements are frequently used to improve saturation modeling in complex situations, often incorporating the concept of wettability. Due to the two methods' distinct tools and physical mechanisms, the interplay of the electrical and magnetic fields and their constitutive equations are generally not addressed. This is directly counter to the situation with the medical imaging modalities, magnetic resonance electrical properties tomography (MREPT) and magnetic resonance electrical impedance tomography (MREIT), where field-specific polarizations and relaxations are used to enhance the contrast. Both electrical and magnetic (EM) fields at the frequencies typically encountered in laboratory and logging environments impart molecular motions impacted by pore structure. In both instances, restricted motions are reflected in their individual responses' time or frequency domain. Using time-domain relaxations and variations in both EM fields, this work focuses on the practicality of using NMR and dielectric relaxation comparisons originally proposed by Bloembergen, Purcell, and Pound (BPP). Similar to the dipolar relaxation equivalence in the BPP model, we develop a relaxation time correlation assuming representative Maxwell-Wagner relaxations for the key pore components demonstrated by Myers. The distributions of dielectric relaxation times evident in carbonate dispersion curves from 1 – 300 MHz were quantified using the Havriliak-Negami (HN) model. The quantifications are then used to evaluate characteristic dielectric dispersions curves generated from a dielectric model introducing multiple pore systems in carbonates. The modeled distributions are spectrally mapped to the NMR T2 distributions based on Debye shielding distances correlated with the conductivity. The interplay of pore connectivity and surface and bulk diffusivity are modeled using a "two-fraction fast exchange model" by Brownstein and Tarr. Using dielectric and NMR experiments along with a combination of micro-CT and SEM imaging techniques, the NMR-based spectral distribution of dielectric relaxation times demonstrates that variable-length scales and fractal dimensions accessed through the dielectric dispersion measurements are more extensive than that implied by the standard reference to the "texture" of a carbonate sample. We also show that the modeled distributions are closely correlated with the conductivity and provide improved petrophysical insight for the frequently used Archie exponent combination (MN) associated with the water tortuosity.
Clegg, Nigel (Halliburton) | Nolan, Seth (Hilcorp Energy Co.) | Duriez, Alban (Halliburton) | Cunha, Katharine (Hilcorp Energy Co.) | Hunter, Lesley (Halliburton) | Wu, Hsu-Hsiang (Halliburton) | Ma, Jin (Halliburton)
Abstract Identifying a well's stratigraphic position from azimuthal electromagnetic (EM) data requires integrating data from multiple depths of investigation. As a well's position within the stratigraphy can be constantly changing, and formations and fluids show considerable lateral variability, this process is difficult to do manually. To simplify this, inversion algorithms are deployed to represent EM logging while drilling (LWD) measurements as models reflecting the geology. Inversion results are not a direct measurement, therefore confidence in the results is critical. Real-time well placement decisions are routinely made on the output of EM inversions. It is critical to understand that these are models, not direct measurements, therefore verification of the results is essential. This paper discusses the workflows and tools available to interrogate the models generated to give high confidence in the results with a focus on a new deep EM tool deployed in a complex geological environment. The deployment of established EM tools in the same bottom hole assembly (BHA) provides independent verification of the results alongside statistical analysis of the inversion. In many complex depositional environments, the resultant geology is not layer cake. Formations can pinch out or show considerable lateral variability. In these environments it is extremely challenging and sometimes impossible to track a single layer or boundary. We examine a case study from Alaska in a complex shallow marine depositional environment. The target sands were expected to show considerable lateral variability with pinch outs and multiple shale lenses and layers. Deployment of a new, deep azimuthal EM tool with an associated inversion algorithm provided a geological model representing the distribution of the target formations. The stratigraphy was comprised of a complex distribution of sands and shales, many penetrated by the wellbore, with others distributed away from the wellbore based on the depth of investigation of the EM measurements. If this model is the primary tool for mapping the formations and steering to penetrate the most productive zones, it is critical to understand the results and have high confidence in them. The second tool in the BHA, the established azimuthal resistivity tool, provided an opportunity to directly compare the azimuthal data with the inversion result from the new tool to critique the inversion results and help to understand this complex geological environment. The complexity of integrating the data from multiple azimuthal images with different depths of investigation, based on multiple transmitter-receiver spacings and transmission frequencies, demonstrates the need for inversion algorithms to convert the EM field data to a simple-to-understand representation of the geology. This case study provides proof of the quality of the model, especially in such a complex geological environment, allowing high confidence in the deployment of this new tool for well path optimization.
Fares, Wael (Halliburton) | Singh, Maniesh (ADNOC Onshore) | Bazuhair, Mohamed (ADNOC Onshore) | Dhermeshwar Thakur, Parmanand (ADNOC Onshore) | N. M. Al Baloushi, Mariam (ADNOC Onshore) | Al Arfi, Saif (ADNOC Onshore) | El Gohary, Mohamed (ADNOC Onshore) | El Abd, Salem (ADNOC Onshore) | S. Al Mesafri, Ahmed (ADNOC Onshore) | Clegg, Nigel (Halliburton) | Walmsley, Arthur (Halliburton) | Aki, Ahmet (Halliburton)
Abstract Drilling horizontal wells in mature fields undergoing enhanced oil recovery programs requires advanced high-resolution reservoir mapping to optimise well placement. Ultra-deep electromagnetic (EM) technology provides shallow and deep 1D and 3D inversion-based mapping in real-time and recorded data. All inversion results show uncertainty in the exact position of formation/fluid boundaries and inverted resistivity values. Understanding this uncertainty and deploying multiple inversions to mitigate it is essential for attaining high confidence in the quality of results. Multi-antenna, azimuthal EM LWD tools propagate EM fields in three dimensions with an ultra-deep depth of investigation (DOI). Robust inversion algorithms both one dimensional (1D) and three dimensional (3D) derive the position and resistivity of formations within the DOI from measurements induced by the propagated fields. This provides geologists with a clearer understanding of the surrounding geology. High confidence in these results, which are models that best represent the EM field is essential. It is vital to understand any uncertainty and where possible use independent verification. Pre-drill modelling provides understanding of the expected response in each formation. Offset data and independent LWD tools provide independent verification of results but have limited DOI's. An understanding of inversion uncertainty is essential to assess quality of the inversions and allow confident geosteering decisions to be made. Pre-drill modeling for a candidate field onshore Abu Dhabi demonstrated the capability of resolving multiple formation layers, with a DOI of more than 90ft. Uncertainty is therefore important as other LWD tools have limited DOI's and can only be used to verify results close to the wellbore. The field trail results exceeded pre-drill expectations, clearly identifying resistivity boundaries, consistent with offset logs. While drilling, the real-time ultra-deep EM tool provided high resolution mapping for precise geosteering within thin layers and mapped a varying water slumping contact 80 ft TVD above the wellbore. A simultaneous 3D EM inversion with 120 ft distance-to-boundary window also imaged the water-front and confirmed that no lateral variation existed in its orientation, it also defined the azimuth, dip and strike of a fault. Confidence in these results was essential as the real-time information helped in timely optimizing completion design to produce oil without water cut and extend the wells production life. Understanding boundary position and resistivity value uncertainty provided confidence in the quality of the results. Post-well these results aided in updating the static model with water flood areas, reservoir tops, faults and overall reservoir structure. The results of this experience provided optimized BHA selection and maximize the benefits of running ultra-deep EM mapping tool in mature fields for multiple purposes; deep reservoir fluid mapping, multi-layered mapping and geosteering within thinner target reservoir units. The confidence in the results allowed important and timely decisions to optimize well position and maximized the hydrocarbon-bearing reservoir contact without exiting.
Summary This study provides an extensive critical review of electromagnetic heating (EMH) methods [inductive heating (IH), low-frequency heating (LFH), and high-frequency heating (HFH)] to highlight their existing challenges in enhanced heavy-oil and oil sands recovery. In general, IH is considered to be less practicable than LFH and HFH. The resistance (ohmic or conduction) heating prevails in LFH while dielectric heating prevails in HFH. Thus, the effectiveness of LFH decreases if reservoir water is overheated to generate steam. Also, the intensity of the energy released and the temperature rise in LFH are not as significant as those in HFH. LFH also fails in penetrating the media with breaks, heterogeneities, and in partially saturated media (e.g., when some oil saturation has been produced). These challenges might somewhat be remedied by HFH at the expense of reducing the electromagnetic (EM) wave penetration depth. The advantages of HFH include remote heating through a desiccated reservoir region around the EM energy source, higher intensity of the energy released and greater temperature rise, and better EM wave penetration through partially saturated media with breaks and heterogeneities. The caveat, however, is that the practical application of HFH could be more expensive than LFH. Besides, the lower depth of EM wave penetration in HFH remains a challenge. During HFH, the temperature increase occurs as a result of the induced molecular rotation in the dielectric material, in particular if the material contains more polar compounds. The polar molecules follow the EM field. This increases the internal molecular friction within the material and generates heat, leading to the rise of temperature. Because the heat generated is a function of the stored (absorbed) energy in the reservoir, the dielectric constant or the real permittivity of the reservoir should be enhanced to enhance the performance of HFH. This ensures that the temperature has risen reasonably in a reasonable amount of time with a reasonable amount of electricity consumption. However, to generate a uniform rise in temperature on a large scale away from the wellbore, the imaginary permittivity of the material should be reasonably lowered, too, for maximizing the penetration of the EM wave (while the real permittivity is an indication of the degree of polarization, the imaginary permittivity is associated with dielectric losses). Lowering the imaginary permittivity away from the wellbore helps minimize the effects of steam condensation (condensate formation retards the EM wave propagation) or delay steam condensation because the reservoir temperature is reduced during the later stages of oil production. The thermal conductivity of the formation should also be enhanced, especially away from the wellbore to generate a more uniform rise in temperature. These three reservoir improvements (enhancing real permittivity, lowering imaginary permittivity, and enhancing thermal conductivity) in an attempt to enhance EMH underpin the rationale behind proposing future optimizations of EMH, and in particular, HFH.
Abstract Electromagnetic devices (EMDs) have been reported to be a method for scale management in the field. One proposed mechanism for scale mitigation is that the device imparts an electromagnetic (EM) pulse that provides sufficient energy to the fluids to cause homogeneous nucleation, resulting in the formation of very small particles (5 to 9 µm), which will pass through the production system, hence preventing/reducing heterogeneous nucleation and deposition on the tubing surface. Previous work revealed that such a device had a measurable impact on a Barium Sulphate scaling system. This paper extends this work by performing visualisation experiments and comparing the results with baseline findings previously published in 2016 using the same test apparatus. Comparative experiments were performed with the device "on" and "off" and images of crystal growth within the test cell taken at regular intervals over the test period. The same conditions as those from 2016 (scaling brines (SR), T, P and Q) were used and then extended by increasing the residence time of the fluid in the test cell by a factor of 2 and 4. The images were analysed using MATLAB to quantify the number of crystals formed, their size and the surface coverage achieved. ESEM images of the test coupon within the cell were also taken for analysis. From all the comparative experiments performed, it was seen that the EMD had a significant, measurable, and positive effect on BaSO4 control with a reduction in crystal growth rate, crystal size and the numbers formed. Comparison with previous data were consistent with results obtained with a lower SR scaling system, suggesting that the EM signal has effectively reduced the surface saturation ratio of the system as it passes through the test cell. Increasing the residence time of the fluid within the EM field enhances the impact of the device in terms of the number of crystals formed, their size and surface coverage within the cell. All these observations support the previously proposed mechanism, that the EMD promotes bulk (homogeneous) nucleation and precipitation, and therefore reduces the energetic favourability of surface (heterogenous) crystal formation.
Guan, Xiaoyue (Chevron) | Li, Gary (Chevron) | Wang, Hanming (Chevron) | Shang, Shubo (Chevron) | Tokar, Timothy (Chevron) | McVey, Kevin (Chevron) | Ovalles, Cesar (Chevron) | Wu, Dagang (University of Houston) | Chen, Ji (University of Houston)
Abstract Radio frequency (RF) heating is recognized as a technique having the potential to thermally enhance remediation of hydrocarbon-impacted soil. RF heating delivers electromagnetic (EM) power to a targeted body of soil, resulting in an increased soil temperature that enhances the in-situ remediation processes such as biodegradation. Antennas are placed either on the ground or installed in the soil near the ground surface. The antennas operate in the hundreds of kHz to MHz range. To model the RF heating process, we successfully coupled a reservoir simulator with a 3-dimensional (3D) EM solver to evaluate the ability of RF technology to heat soil in situ. The coupled reservoir/EM simulator solves the EM fields and associated heating for a heterogeneous reservoir or soil volume in the presence of multiple antennas. The coupling was accomplished through a flexible interface in the reservoir simulator that allows the runtime loading of third-party software libraries with additional physics. This coupled workflow had been previously used for studying RF heating for heavy oil recovery (Li 2019). An RF heating simulation case study was performed in support of a soil remediation field test designed to demonstrate the ability to heat soils using EM energy. The study included field test data analysis, simulation model building, and history matching the model to test data. Results indicate, on average, the soil was heated ∼2-3°C above the initial formation temperature after approximately two days (52 hours) of RF heating. We found that the RF heating was local, and our simulation model, after tuning input parameters, was able to predict a temperature profile consistent with the field test observations. With properly designed RF heating field pilots and tuning of EM and reservoir parameters in simulation models, the coupled reservoir/EM simulator is a powerful tool for the calibration, evaluation, and optimization of RF heating operations.
The simulation of time-domain electromagnetic fields often uses the technique of time stepping. A time stepping is a sequential computing process, as early times must be stepped through before late times. In this paper, we propose an approach to parallelize the calculation in time domain for an improved efficiency. Our approach, based on the theory of survey decomposition and the concept of scale matching, computes the time-domain responses of each time channel separately in parallel. The computational efficiency comes from two ideas. First, time-domain EM fields diffuse at later times, so each time channel can be stepped at a step length adapted to its temporal scale. Second, simulation of the entire on-time waveform may not be necessary – early time channels only need a narrow portion of the pulse width. Exploiting those two properties, our approach first empirically determines a characteristic step length (Δt) for a particular time channel. Then a step-off response is obtained by stepping at a constant Δt; the discrete impulse response can be calculated by differentiating the step-off response by Δt. Finally, the discrete impulse response is convolved with the effective part of the source waveform discretized by Δt. By experimenting with four airborne TEM waveforms and a large number of random models, we obtain a set of empirical parameters for Δt and for the effective pulse width. The improvement in efficiency is demonstrated by a 3D time-domain EM example.