Characterization of the Measurement Error in Time-Lapse Seismic Data and Production Data with an EM Algorithm

We have developed an efficient approach of petroleum reservoir model calibration that integrates 4D seismic surveys together with well-production data. The approach is particularly well-suited for the calibration of high-resolution reservoir properties (permeability) because the field-scale seismic data are areally dense, whereas the production data are effectively averaged over interwell spacing. The joint calibration procedure is performed using streamline-based sensitivities derived from finite-difference flow simulation. The inverted seismic data (i.e., changes in elastic impedance or fluid saturations) are distributed as a 3D high-resolution grid cell property. The sensitivities of the seismic and production surveillance data to perturbations in absolute permeability at individual grid cells are efficiently computed via semianalytical streamline techniques. We generalize previous formulations of streamline-based seismic inversion to incorporate realistic field situations such as changing boundary conditions due to infill drilling, pattern conversion, etc. A commercial finite-difference flow simulator is used for reservoir simulation and to generate the time-dependent velocity fields through which streamlines are traced and the sensitivity coefficients are computed. The commercial simulator allows us to incorporate detailed physical processes including compressibility and nonconvective forces, e.g., capillary pressure effects, while the streamline trajectories provide a rapid evaluation of the sensitivities. The efficacy of our proposed approach was tested with synthetic and field applications. The synthetic example was the Society of Petroleum Engineers benchmark Brugge field case. The field example involves waterflooding of a North Sea reservoir with multiple seismic surveys. In both cases, the advantages of incorporating the time-lapse variations were clearly demonstrated through improved estimation of the permeability heterogeneity, fluid saturation evolution, and swept and drained volumes. The value of the seismic data integration was in particular proven through the identification of the continuity in reservoir sands and barriers, and by the preservation of geologic realism in the calibrated model.

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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

Interpretation ◽

10.1190/int-2020-0153.1 ◽

2021 ◽

pp. 1-53

Author(s):

Matthew Bray ◽

Jakob Utley ◽

Yanrui Ning ◽

Angela Dang ◽

Jacquelyn Daves ◽

...

Keyword(s):

Hydraulic Fracturing ◽

Seismic Data ◽

Time Lapse ◽

Production Performance ◽

P Wave ◽

Production Data ◽

Total Production ◽

Wattenberg Field ◽

Reservoir Models ◽

3D Geomechanical Model

Enhanced hydrocarbon recovery is essential for continued economic development of unconventional reservoirs. Our study focuses on dynamic characterization of the Niobrara and Codell Formations in Wattenberg Field through the development and analysis of a full integrated reservoir model. We demonstrate the effectiveness of 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 non-stimulated and non-producing 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 utilizing pre-stack 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. Additionally, structural and hydraulic complexity associated with faults generate spatial variability in a well’s total production.

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Time-lapse seismic history matching with an iterative ensemble smoother and deep convolutional autoencoder

Geophysics ◽

10.1190/geo2019-0019.1 ◽

2019 ◽

Vol 85 (1) ◽

pp. M15-M31 ◽

Cited By ~ 4

Author(s):

Mingliang Liu ◽

Dario Grana

Keyword(s):

Data Assimilation ◽

Seismic Data ◽

History Matching ◽

Time Lapse ◽

Production Data ◽

Data Set ◽

Reservoir Models ◽

Ensemble Smoother ◽

Convolutional Autoencoder ◽

Seismic Measurements

We have developed a time-lapse seismic history matching framework to assimilate production data and time-lapse seismic data for the prediction of static reservoir models. An iterative data assimilation method, the ensemble smoother with multiple data assimilation is adopted to iteratively update an ensemble of reservoir models until their predicted observations match the actual production and seismic measurements and to quantify the model uncertainty of the posterior reservoir models. To address computational and numerical challenges when applying ensemble-based optimization methods on large seismic data volumes, we develop a deep representation learning method, namely, the deep convolutional autoencoder. Such a method is used to reduce the data dimensionality by sparsely and approximately representing the seismic data with a set of hidden features to capture the nonlinear and spatial correlations in the data space. Instead of using the entire seismic data set, which would require an extremely large number of models, the ensemble of reservoir models is iteratively updated by conditioning the reservoir realizations on the production data and the low-dimensional hidden features extracted from the seismic measurements. We test our methodology on two synthetic data sets: a simplified 2D reservoir used for method validation and a 3D application with multiple channelized reservoirs. The results indicate that the deep convolutional autoencoder is extremely efficient in sparsely representing the seismic data and that the reservoir models can be accurately updated according to production data and the reparameterized time-lapse seismic data.

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Reconciling Time-lapse Seismic Data and Production Data: Application to a Gas Field

10.2118/56732-ms ◽

1999 ◽

Author(s):

Xuri Huang ◽

Robert Will ◽

Mashiur Khan ◽

Larry Stanley

Keyword(s):

Seismic Data ◽

Time Lapse ◽

Production Data ◽

Gas Field ◽

Data Application

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Streamline-Based Time-Lapse-Seismic-Data Integration Incorporating Pressure and Saturation Effects

SPE Journal ◽

10.2118/166395-pa ◽

2017 ◽

Vol 22 (04) ◽

pp. 1261-1279 ◽

Cited By ~ 10

Author(s):

Shingo Watanabe ◽

Jichao Han ◽

Gill Hetz ◽

Akhil Datta-Gupta ◽

Michael J. King ◽

...

Keyword(s):

High Resolution ◽

Finite Difference ◽

Seismic Data ◽

History Matching ◽

Flow Simulation ◽

Time Lapse ◽

Production Data ◽

Reservoir Properties ◽

Fluid Saturation ◽

Saturation Effects

Summary We present an efficient history-matching technique that simultaneously integrates 4D repeat seismic surveys with well-production data. This approach is particularly well-suited for the calibration of the reservoir properties of high-resolution geologic models because the seismic data are areally dense but sparse in time, whereas the production data are finely sampled in time but spatially averaged. The joint history matching is performed by use of streamline-based sensitivities derived from either finite-difference or streamline-based flow simulation. For the most part, earlier approaches have focused on the role of saturation changes, but the effects of pressure have largely been ignored. Here, we present a streamline-based semianalytic approach for computing model-parameter sensitivities, accounting for both pressure and saturation effects. The novelty of the method lies in the semianalytic sensitivity computations, making it computationally efficient for high-resolution geologic models. The approach is implemented by use of a finite-difference simulator incorporating the detailed physics. Its efficacy is demonstrated by use of both synthetic and field applications. For both the synthetic and the field cases, the advantages of incorporating the time-lapse variations are clear, seen through the improved estimation of the permeability distribution, the pressure profile, the evolution of the fluid saturation, and the swept volumes.

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Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

SPE Reservoir Evaluation & Engineering ◽

10.2118/131538-pa ◽

2011 ◽

Vol 14 (05) ◽

pp. 621-633 ◽

Cited By ~ 3

Author(s):

Alireza Kazemi ◽

Karl D. Stephen ◽

Asghar Shams

Keyword(s):

Seismic Data ◽

History Matching ◽

Time Lapse ◽

Production Data ◽

Similar Degree ◽

Full Field ◽

Matching Process ◽

Production History ◽

Induced Changes ◽

4D Seismic

Summary History matching of a reservoir model is always a difficult task. In some fields, we can use time-lapse (4D) seismic data to detect production-induced changes as a complement to more conventional production data. In seismic history matching, we predict these data and compare to observations. Observed time-lapse data often consist of relative measures of change, which require normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching. We apply the approach to the Nelson field in which four surveys are available over 9 years of production. We normalize the 4D signature in a number of ways. First, we use predictions of 4D signature from vertical wells that match production, and we derive a normalization function. As an alternative, we use crossplots of the full-field prediction against observation. Normalized observations are used in an automatic-history-matching process, in which the model is updated. We analyze the results of the two normalization approaches and compare against the case of just using production data. The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8 and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods, respectively. When we use only production data, the production history match is improved to a similar degree (45%), but in predictions, the improvement is only 25% and the 4D seismic misfit is 10% worse. Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching, we can better predict reservoir behavior and identify regions of remaining oil.

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Time-lapse difference inversion based on the modified reflectivity method with differentiable Hyper-Laplacian blocky constraint

Geophysics ◽

10.1190/geo2020-0153.1 ◽

2021 ◽

pp. 1-121

Author(s):

Wei Tang ◽

Jingye Li ◽

Wenbiao Zhang ◽

Jian Zhang ◽

Weiheng Geng ◽

...

Keyword(s):

Seismic Data ◽

Time Lapse ◽

Inversion Method ◽

Transmission Losses ◽

Bayesian Theorem ◽

Reflectivity Method ◽

Fluid Properties ◽

Internal Multiples ◽

Fine Tune

Time-lapse (TL) seismic has great potential in monitoring and interpreting time-varying variations in reservoir fluid properties during hydrocarbon exploitation. Obtaining the disparities of reservoir elastic parameters by inversion is essential for TL reservoir monitoring. Conventional TL inversion is carried out by stages without coupling processing and leads to inaccuracy of the results. We directly use the differences in seismic data responses from different vintages, namely difference inversion, to improve the results credibility. It may reduce the deviations of the subtraction of base and monitor inversions in traditional methods. Moreover, most existing studies involving pre-stack inversion methods use the Zoeppritz equation or its approximants, which failed to consider the wave propagation effects. Here, we propose a new TL difference inversion based on the modified reflectivity method (MRM), the internal multiples and transmission losses are taken into consideration to fine-tune the characterization of the seismic wave propagating underground. The new method is modified on the basis of reflectivity method (RM) making it feasible in TL difference inversion, and derived from the Bayesian theorem. For further delineating the boundaries between layers, the differentiable Hyper-Laplacian blocky constraint (DHLBC) is introduced into the prior information of Bayesian framework, which heightens the sparseness in the vertical gradients of inversion results and leads to sharp interlayer boundaries of difference parameters. The synthetic and field data examples demonstrate that the proposed TL difference inversion method has clear advantages in accuracy and resolution compared to Zoeppritz method and MRM without DHLBC.

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Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽

10.3390/en14041052 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1052

Author(s):

Baozhong Wang ◽

Jyotsna Sharma ◽

Jianhua Chen ◽

Patricia Persaud

Keyword(s):

Machine Learning ◽

Random Forest ◽

Reservoir Characterization ◽

Time Lapse ◽

Production Data ◽

Oil Saturation ◽

Ensemble Machine Learning ◽

Input Parameters ◽

Saturation Profiles ◽

Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

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