Time-lapse Seismic Data Research Articles

Data-driven prestack-waveform inversion using genetic algorithm: Methodology and examples

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data-driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizontal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudowells by running a two-pass 1D isotropic elastic prestack-waveform inversion that uses the reflectivity method for forward modeling and the genetic algorithm for optimization. We then use the estimated pseudowells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geologic settings, we determine the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is, however, necessary to improve the computational efficiency of the methodology.

Time-lapse seismic data reconstruction using compressive sensing

The time-lapse seismic method plays a critical role in reservoir monitoring and characterization. However, time-lapse data acquisitions are costly. Sparse acquisitions combined with postacquisition data reconstruction could reduce costs and facilitate more frequent applications of the time-lapse seismic monitoring. We have developed a sparse time-lapse seismic data reconstruction methodology based on compressive sensing. The method works with a hybrid of repeated and nonrepeated sample locations. To make use of the additional information from nonrepeated locations, we develop a view that nonrepeated samples in space are equivalent to irregular samples in calendar time. Therefore, we use these irregular samples in time coming from nonrepeated samples in space to improve the performance of compressive sensing reconstruction. The tests on synthetic and field data sets indicate that our method can achieve a sufficiently accurate reconstruction by using as few as 10% of the receivers or traces. The method not only works with spatially irregular sampling for dealing with the land accessibility problem and for reducing the number of nodal sensors, but it also uses the nonrepeated measurements to improve reconstruction accuracy.

Multidisciplinary analysis of hydraulic stimulation and production effects within the Niobrara and Codell reservoirs, Wattenberg Field, Colorado — Part 2: Analysis of hydraulic fracturing and production

Enhanced hydrocarbon recovery is essential for continued economic development of unconventional reservoirs. We have focused on dynamic characterization of the Niobrara and Codell Formations in Wattenberg Field through the development and analysis of a full integrated reservoir model. We determine the effectiveness of the hydraulic fracturing and production with two seismic monitor surveys, surface microseismic, completion data, and production data. The two monitor surveys were recorded after stimulation and again after two years of production. Identification of reservoir deformation due to hydraulic fracturing and production improves reservoir models by mapping nonstimulated and nonproducing zones. Monitoring these time-variant changes improves the prediction capability of reservoir models, which in turn leads to improved well and stage placement. We quantify dynamic reservoir changes with time-lapse P-wave seismic data using prestack inversion and velocity-independent layer stripping for velocity and attenuation changes within the Niobrara and Codell reservoirs. A 3D geomechanical model and production data are history matched, and a simulation is run for two years of production. Results are integrated with time-lapse seismic data to illustrate the effects of hydraulic fracturing and production. Our analyses illustrate that chalk facies have significantly higher hydraulic fracture efficiency and production performance than marl facies. In addition, structural and hydraulic complexity associated with faults generate spatial variability in a well’s total production.

Multidisciplinary analysis of hydraulic stimulation and production effects within the Niobrara and Codell reservoirs, Wattenberg Field, Colorado — Part 1: Baseline reservoir conditions

In the Wattenberg Field, the Reservoir Characterization Project at the Colorado School of Mines and Occidental Petroleum Corporation (Oxy) (formerly the Anadarko Petroleum Corporation) collected time-lapse seismic data for characterization of changes in the reservoir caused by hydraulic fracturing and production in the Niobrara Formation and Codell Sandstone member of the Carlile Formation. We have acquired three multicomponent seismic surveys to understand the dynamic reservoir changes caused by hydraulic fracturing and production of 11 horizontal wells within a 1 mi2section (the Wishbone Section). The time-lapse seismic survey acquisition occurred immediately after the wells were drilled, another survey after stimulation, and a third survey after two years of production. In addition, we integrate core, petrophysical properties, fault and fracture characteristics, as well as P-wave seismic data to illustrate reservoir properties prior to simulation and production. Core analysis indicates extensive amounts of bioturbation in zones of high total organic content (TOC). Petrophysical analysis of logs and core samples indicates that chalk intervals have high amounts of TOC (>2%) and the lowest amount of clay in the reservoir interval. Core petrophysical characterization included X-ray diffraction analysis, mercury intrusion capillary pressure, N2gas adsorption, and field emission scanning electron microscopy. Reservoir fractures follow four regional orientations, and chalk facies contain higher fracture density than marl facies. Integration of these data assist in enhanced well targeting and reservoir simulation.

Nonrepeatability effects on time-lapse 4D seismic full-waveform inversion for ocean-bottom node data

Full-waveform inversion (FWI) can be applied to time-lapse (4D) seismic data for subsurface reservoir monitoring. However, nonrepeatability (NR) issues can contaminate the data and cause artifacts in the estimation of 4D rock and fluid property changes. Therefore, evaluating and studying the NR effects on the 4D data and FWI results can help, for instance, discriminate inversion artifacts from true changes and guide seismic survey design and processing workflows. Using realistic reservoir models, data, and field measurements of NR, we find the effects of NR source-receiver position and seawater velocity changes on the data and the 4D FWI results. We show that ignoring these NR effects in the inversion can cause strong artifacts in the estimated velocity change models and thus should be addressed before or during inversion. The NR source-receiver positioning issue can be successfully addressed by 4D FWI, whereas the NR water velocity issue requires measurements or estimations of water velocities. Furthermore, we compare the accuracy and robustness of the parallel, double-difference, and central-difference 4D FWI methods to realistic NR ocean-bottom node data in a quantitative way. Parallel 4D FWI fails to capture geomechanical changes and also overestimates the aquifer layer changes with NR data. Double-difference 4D FWI is capable of recovering the geomechanical changes, but it is also sensitive to NR noises, generating more artifacts in the overburden. By averaging the forward and reverse bootstrap 4D estimates, central-difference 4D FWI is more robust to NR noises and also produces the most accurate 4D estimates.

Dynamic Characterization of Reservoirs Constrained by Time-Lapse Prestack Seismic Inversion

Dynamic changes of reservoir parameters within a reservoir are usually estimated by a history matching process based on the production data. However, it is difficult to accurately acquire the spatial distribution using production data as the constraints. We formulate a nonlinear inversion method to dynamically characterize the reservoir parameter variations from time-lapse prestack seismic data. Besides, we introduce the modified Hertz-Mindlin (H-M) model to simulate the changes in the elastic behavior of a turbidite sandstone during production by establishing a link between reservoir parameters and elastic parameters. Then, the exact Zoeppritz equation is exploited as a forward engine to convert these parameters into synthetic time-lapse seismic data. Numerical examples verify that the porosity parameter has a higher reflection sensitivity than the other two parameters, which is followed by the effective pressure. The water saturation parameter, however, is the least sensitive. Following a Bayesian approach, the baseline and monitor seismic data are used in the seismic inversion to establish the regularized augmented function. Combining the modified H-M rock physics model with the exact Zoeppritz equation as a forward operator, a regularized function is minimized with the Gauss-Newton optimization method to determine a model update. We further apply the proposed inversion algorithm to a real seismic data set, including baseline and monitor seismic data. The results demonstrate that the proposed inversion method can not only yield an accurate description of subsurface static reservoir properties but also improve the accuracy of dynamic reservoir parameter characterization.

Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or in locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki Basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared with those same attributes computed on broadband seismic data. We have developed an unsupervised machine-learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOMs). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes indicate internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared with production data. The SOM classifications of the Maui 4D seismic data seem to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

Target-oriented time-lapse waveform inversion using deep learning-assisted regularization

Detection of the property changes in the reservoir during injection and production is important. However, the detection process is very challenging using surface seismic surveys because these property changes often induce subtle changes in the seismic signals. The quantitative evaluation of the subsurface property obtained by full-waveform inversion allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational cost. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We have applied a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small zone of interest. The redatuming technique generates time-lapse virtual data for the target-oriented inversion. Considering that the injection and production wells are often present in the target zone, we can incorporate the well velocity information with the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network to learn the statistical relationship between the inverted model and the facies interpreted from well logs. The trained network is used to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes. The numerical results demonstrate that our method is capable of inverting for the time-lapse property changes effectively in the target zone by incorporating the learned model information from well logs.

CO2 storage monitoring based on time-lapse seismic data via deep learning

CO2 geological storage is an effective way of reducing atmospheric CO2 concentration. The physical characteristics of the reservoir and the feasibility of CO2 flooding were analyzed based on the carbon capture, utilization, and storage (CCUS) project in Yanchang Oilfield, China. The heterogeneity of the reservoir indicates variation in the shape of the CO2 plume. A CO2 storage monitoring method based on a full convolutional neural network (FCN) that applies time-lapse difference seismic data is proposed for the geological task of CO2 storage monitoring. A total of 2800 sets of forward simulation time-lapse difference data and corresponding velocity changes were used to train the network. These data include noisy and noise-free data, which help improve the noise immunity of the network. The prediction accuracy of the network under different samples of training data, noise levels, and velocity changes is discussed. Furthermore, 100 sets of noisy data and 100 sets of noise-free data were used to verify the predictive ability of the network. The test results of the synthetic data show that the FCN-based CO2 storage monitoring method exhibits relatively high efficiency and high precision compared with time-lapse full waveform inversion.

Near-surface real-time seismic imaging using parsimonious interferometry

Results are presented for real-time seismic imaging of subsurface fluid flow by parsimonious refraction and surface-wave interferometry. Each subsurface velocity image inverted from time-lapse seismic data only requires several minutes of recording time, which is less than the time-scale of the fluid-induced changes in the rock properties. In this sense this is real-time imaging. The images are P-velocity tomograms inverted from the first-arrival times and the S-velocity tomograms inverted from dispersion curves. Compared to conventional seismic imaging, parsimonious interferometry reduces the recording time and increases the temporal resolution of time-lapse seismic images by more than an order-of-magnitude. In our seismic experiment, we recorded 90 sparse data sets over 4.5 h while injecting 12-tons of water into a sand dune. Results show that the percolation of water is mostly along layered boundaries down to a depth of a few meters, which is consistent with our 3D computational fluid flow simulations and laboratory experiments. The significance of parsimonious interferometry is that it provides more than an order-of-magnitude increase of temporal resolution in time-lapse seismic imaging. We believe that real-time seismic imaging will have important applications for non-destructive characterization in environmental, biomedical, and subsurface imaging.

Subspace Ensemble Randomized Maximum Likelihood with Local Analysis for Time-Lapse-Seismic-Data Assimilation

Summary Time-lapse-seismic-data assimilation has been drawing the reservoir-engineering community's attention over the past few years. One of the advantages of including this kind of data to improve the reservoir-flow models is that it provides complementary information compared with the wells' production data. Ensemble-based methods are some of the standard tools used to calibrate reservoir models using time-lapse seismic data. One of the drawbacks of assimilating time-lapse seismic data involves the large data sets, mainly for large reservoir models. This situation leads to high-dimensional problems that demand significant computational resources to process and store the matrices when using conventional and straightforward methods. Another known issue associated with the ensemble-based methods is the limited ensemble sizes, which cause spurious correlations between the data and the parameters and limit the degrees of freedom. In this work, we propose a data-assimilation scheme using an efficient implementation of the subspace ensemble randomized maximum likelihood (SEnRML) method with local analysis. This method reduces the computational requirements for assimilating large data sets because the number of operations scales linearly with the number of observed data points. Furthermore, by implementing it with local analysis, we reduce the memory requirements at each update step and mitigate the effects of the limited ensemble sizes. We test two local analysis approaches: one distance-based approach and one correlation-based approach. We apply these implementations to two synthetic time-lapse-seismic-data-assimilation cases, one 2D example, and one field-scale application that mimics some of the real-field challenges. We compare the results with reference solutions and with the known ensemble smoother with multiple data assimilation (ES-MDA) using Kalman gain distance-based localization. The results show that our method can efficiently assimilate time-lapse seismic data, leading to updated models that are comparable with other straightforward methods. The correlation-based local analysis approach provided results similar to the distance-based approach, with the advantage that the former can be applied to data and parameters that do not have specific spatial positions.

Reprocessed time-lapse seismic data provides new reservoir information on the Njord field

Improvements in time-lapse (4D) seismic processing methods provide opportunities to extract additional information from older time-lapse seismic data sets. This new information can help to optimize reservoir production. Four marine streamer time-lapse seismic data sets on the Njord field, offshore mid-Norway, were previously processed in 2007. Reprocessing in 2020 concentrated on better noise and multiple attenuation, improved imaging, and a focused step-by-step workflow to estimate and correct timing variations within and between surveys. The results dramatically improved on the 2007 processing. Gas injection could be tracked in greater detail and previously unidentified 4D signal was interpreted. The new data sets are expected to enhance reservoir understanding and improve future field performance.

Influence of reservoir-scale heterogeneities on the growth, evolution and migration of a CO2 plume at the Sleipner Field, Norwegian North Sea

New analysis of the baseline (pre-injection) seismic data at Sleipner has revealed large-scale, roughly north-trending, channelled ‘fairways’ at a range of stratigraphical levels in the Utsira Sand. The baseline data also reveal localised stratigraphical ‘point discontinuities’ within the reservoir, some of which show evidence of having provided vertical conduits for earlier natural gas flow. The repeat time-lapse seismic datasets, where finer details of reservoir geology are illuminated by the reflective CO2, show smaller-scale, north-trending and sometimes sinuous channels within the larger channel fairways. They also show a number of vertical CO2 pathways within the CO2 plume, corresponding to the point discontinuities seen on the baseline data.Reservoir flow models were set up with flow properties constrained only by the observed levels of CO2 accumulation in the reservoir and the arrival time of CO2 at the reservoir top just prior to the first repeat seismic survey in 1999. The initial model with laterally homogeneous sand units separated by thin semi-permeable mudstones achieved only a moderate match to the observed time-lapse seismic data. Subsequent flow models, progressively incorporating higher permeability vertical CO2 pathways through the mudstones and large-scale channel fairways within the reservoir sands, yielded a progressive and marked improvement in the history-match of key CO2 layers within the plume. Crucially, no layer-specific model calibration was employed to achieve this improvement.New geophysical measurements from Utsira Sand core have recently become available. These measurements provide important constraints on rock physics models of CO2 and brine mixtures in the Utsira Sand. An empirical Brie fluid mixing law for intermediate fluid saturations provides a good fit to the new laboratory data, allowing measurements of CO2 saturation to be converted into seismic velocity. This rock physics model was used to convert CO2 saturation distributions predicted by the most realistic reservoir model into a seismic velocity model of the CO2 plume. Synthetic seismic reflectivity profiles generated using this velocity model show a striking resemblance to the observed time-lapse seismic data, both in terms of plume layer reflectivity and also of time-shifts within and beneath the CO2 plume. This provides confidence in the fidelity of the preferred reservoir model solution.These results represent a significant breakthrough in the understanding and modelling of CO2 plume development at Sleipner. We emphasise that the improvements were brought about not by fine tuning reservoir properties to fit the observed time-lapse data, but simply by incorporating geological permeability features that can reasonably be inferred from the baseline seismic data.

Feasibility of using the P-Cable high-resolution 3D seismic system in detecting and monitoring CO2 leakage

The P-Cable technology is an acquisition principle for high-resolution and ultra-high-resolution 3D seismic data. Many 3D seismic datasets have been acquired over the last decade, but the application in time-lapse studies for monitoring of CO2 storage is a new and intriguing topic. High-resolution 3D (HR3D) seismic has the potential to detect and monitor CO2 leakage at carbon capture and storage sites with higher accuracy at depths ∼0−2 km below the seafloor compared to more traditional conventional seismic time-lapse data. Here, we synthesize and evaluate research on detection of subsurface CO2 movement using the P-Cable system and address the comparative advantages and disadvantages of conventional and HR3D technologies for subsurface fluid migration monitoring. Studies on P-Cable 4D seismic data show good repeatability (NRMS, 10–40 %), indicating a future monitoring potential. Analysis of detection limits of CO2 data from a CO2 storage site show the ability to detect very small amounts of CO2 (1.3–10.6 t; 3.3–27.4 % gas saturation) in the shallow subsurface. These detection limits are ∼30−300 times smaller than the detection limits of conventional seismic data at similar depths. We conclude that the P-Cable acquisition system can be a valuable monitoring tool in detecting small leakages and can complement conventional seismic data monitoring of the deeper interval.

Bayesian Inversion of Time-Lapse Seismic AVO Data for Multimodal Reservoir Properties

We consider time-lapse seismic amplitude versus offset (AVO) inversion for the reservoir properties porosity and water saturation, with a focus on monitoring the evolution of water saturation in a dynamic setting of ongoing production. We operate in a Bayesian framework based on the integration of seismic AVO modeling and rock physics relations. One major challenge in the inversion of seismic data for reservoir properties is the multimodality of these properties. Fluid saturation is generally bimodal due to the gravity effect, and often distinctly so, with abrupt spatial mode transitions. The novelty of the proposed approach is the assumption of a selection Gaussian random field (S-GRF) for the prior spatial model of porosity and water saturation, which can represent the multimodal characteristics of these reservoir properties. The likelihood model is Gauss-linear and based on linearized seismic and rock physics models, which entails that the associated posterior model is also an S-GRF, with analytically assessable parameters. Hence, the posterior model is capable of representing multimodality and abrupt spatial mode transitions. Two realistic case studies are considered; the production of an oil reservoir in the North Sea, and the injection of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> into a subsurface potential CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> storage unit. Focus is on the movement of the oil–water-contacts along a vertical profile in the first case, and on the expansion of the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> region in a cross section in the other, both of which can be inferred from the changes in the water saturations. The results are considered to be very encouraging and the proposed statistical formulation appears to be particularly well suited for fluid monitoring problems of the described type.

Monitoring of seismic attenuation change associated with vapor-liquid phase transition using time-lapse reflection seismic data in Kakkonda geothermal field, Japan

Seismic wave attenuation can potentially be used for the investigation of the fluid phase and flow properties in a fractured reservoir. However, seismic attenuation estimation is easily affected by various factors, such as tuning effects, source and receiver coupling, and effects from near-surface weathering layers. Time-lapse seismic attenuation estimation can avoid these problems because the above factors are canceled between surveys. We conducted time-lapse attenuation estimation using three reflection seismic surveys with different timings acquired in the Kakkonda geothermal field during a 12-day period spanning a well shut-in period. To investigate geothermal fluid changes associated with increased pore pressure, we propose an approach for estimating attenuation changes between time-lapse seismic surveys. Firstly, three seismic reflection sections were obtained from the corresponding time-lapse seismic data using the modified prestack time migration method, which can handle the uncertainties of migration velocity estimation in a highly fractured and structurally very complex medium, for which accurate velocity estimation is difficult to achieve using conventional methods. Then, time-lapse attenuation was evaluated for two of the three seismic reflection sections using the spectral ratio and the centroid frequency shift methods. Numerical modeling was conducted to verify our proposed procedure. Careful preprocessing was carried out to enhance the repeatability of time-lapse seismic data, especially on the frequency content, which is indirectly related to seismic attenuation. Finally, we demonstrated an increase of attenuation associated with an increase of pore pressure in and around the known super-shallow vapor-dominated reservoir during a short period (7 days) spanning from one day before the shut-in of producing well to a week after that. A possible cause of this short-period attenuation increase could be the vapor-liquid phase transition in the reservoir.

Joint Impedance and Facies Inversion of Time-lapse Seismic Data for Improving Monitoring of CO2 Incidentally Stored From CO2 EOR

Time-lapse seismic monitoring is an effective and proven technology for determining the distribution of CO2 in a subsurface reservoir. When injected CO2 displaces other reservoir fluids, the seismic impedance changes, causing time-lapse seismic amplitude differences in the injection zones. The analysis and interpretation of images created from these amplitude differences can provide information about reservoir architecture and the CO2 migration within the reservoir. Incorporating seismic inversion and rock physics into the interpretation of time-lapse seismic data can considerably improve the modeling and monitoring to detect and assess the location of CO2 over time. The joint inversion method presented in this paper is based on innovative approaches. The method has an integral representation of the geology in the inversion algorithm using elastic facies, which provides information about the spatial distribution of the geologic heterogeneities controlling the spreading of fluids in the reservoir. The method was successfully applied to time-lapse seismic data from a mature oil field undergoing CO2 enhanced oil recovery. The estimated seismic acoustic impedances and facies reflect the characteristics of individual geologic facies and fluid conditions of the reservoir subject to CO2 injection. The probabilities estimated by the joint impedance and facies inversion for the reservoir’s litho-fluid facies can be used for forecasting CO2 saturation and pressure changes within the target reservoir.

Time-Lapse Seismic Difference-and-Joint Prestack AVA Inversion

Time-lapse (4-D) seismic exploration is one of the essential means for accurately reconstructing the underground geological model, enhancing oil/gas recovery (EOR), and predicting remaining oil distribution. Under the assumption that the rock skeleton is almost unchanged in the process of development, inversion of time-lapse seismic difference data can be used to characterize the dynamic reservoir parameter. However, the inaccuracy of the forward operator, lack of constraints, and inappropriate regularization weight may lead to ill-posedness. Thus, the ill-posedness is still a key factor affecting the accuracy and stability of time-lapse seismic inversion. In this article, an improved difference-and-joint inversion strategy is proposed using the modified linear approximation as the forward operator. First, based on the modified approximation as the forward operator, time-lapse seismic difference inversion is exploited to obtain the variations of elastic parameter reflectance caused by the production-induced model perturbation. Then, we innovatively proposed to take the production-induced model perturbation as constraints, thereby achieving more accurate geological models for multiperiod seismic data joint inversion. Moreover, the L-curve method is exploited in this article to acquire the optimal regularization weight adaptively in each iteration step. Finally, the difference inversion results are used as constraints to precisely invert the geological model by combining multiperiod seismic data.

Feature-Oriented Joint Time-Lapse Seismic and Electromagnetic History Matching Using Ensemble Methods

SummaryWe propose a feature-oriented ensemble history-matching workflow with a focus on the integration of time-lapse seismic and electromagnetic (EM) data. The developed workflow consists of two main steps. First, the rock cross-properties, such as water saturation and porosity relating to both seismic and EM attributes, are estimated by joint inversion of seismic and EM data using an iterative ensemble smoother (ES). Second, the remaining model parameters of interest, such as permeability, are calibrated using the updated cross-properties. To assimilate the inverted-saturation information efficiently, we take a feature-oriented integration approach in which front positions are identified from the inverted-saturation field. Related model parameters are then conditioned to the interpreted fronts using the iterative ES with a distance parameterization. The novelty of the proposed approach consists of combining the feature-oriented history matching with ensemble-based geophysical inversion to achieve an efficient joint integration of multiple sources of geophysical data. The performance of the proposed history-matching workflow is examined using a 2D channelized reservoir model and a more-realistic 3D reservoir model with a crosswell configuration for seismic and EM surveys. It is demonstrated that the developed workflow provides a novel and effective way to calibrate reservoir models with multiple sources of geophysical data. The experimental results show a positive synergy effect on the characterization of model variables by jointly assimilating seismic and EM data.

First time-lapse OBN in Bonga deepwater offshore field

Time-lapse seismic data from Bonga Field, located in deepwater Nigeria, have delivered excellent results previously from dedicated streamer seismic surveys (2000, 2008, and 2012) that image changes in amplitude due to water replacing oil. One challenge with the streamer data is the presence of a floating production storage and offloading (FPSO) unit. It is difficult to accurately repeat streamer acquisition geometry in economically important updip regions of reservoirs that lie beneath the FPSO. To ensure an accurate repeat of this area, we acquired an ocean-bottom node (OBN) survey in 2010 and carried out the first time-lapse OBN repeat of the survey in 2018. Time-lapse processing of the OBN data produced excellent results. We obtained an OBN-on-OBN normalized root mean square (NRMS) difference repeatability of 6%. This was an improvement over the 12% NRMS for streamer-on-streamer repeats. The OBN data quality was unaffected by the FPSO, enabling us to properly image the changes occurring in this area. The 4D OBN interpretation was used to identify bypassed oil opportunities. The data also were used to derisk well placement, optimize water injection, and update the dynamic reservoir models. The new models are essential to enhance predictability and field performance. We also analyzed an area northwest of the field where we extended the 2018 OBN acquisition and compared it to streamer data to optimize a new injection well location.