Optimal model parameterization in stochastic inversion for reservoir properties using time‐lapse seismic and production data

2007 ◽  
Author(s):  
Long Jin ◽  
Mrinal. K. Sen ◽  
Paul. L. Stoffa ◽  
Roustam K. Seif
Keyword(s):  
Time Lapse ◽  
Production Data ◽  
Optimal Model ◽  
Reservoir Properties ◽  
Stochastic Inversion ◽  
Model Parameterization

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

SPE Journal ◽  
2017 ◽  
Vol 22 (04) ◽  
pp. 1261-1279 ◽  
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.

Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. O9-O17 ◽  
Author(s):  
Upendra K. Tiwari ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Elastic Model ◽  
Quality Factors ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Estimated Parameters ◽  
Model Parameterization ◽  
The Earth ◽  
Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

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

Energies ◽  
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.

Application of Multiple Diagnostic Plots to Identify End of Linear Flow in Unconventional Reservoirs

2021 ◽  
Author(s):  
Helmi Pratikno ◽  
W. John Lee ◽  
Cesario K. Torres
Keyword(s):  
Material Balance ◽  
Unconventional Reservoirs ◽  
Production Data ◽  
Reservoir Properties ◽  
Type Curve ◽  
Linear Flow ◽  
Diagnostic Plots ◽  
Actual Time ◽  
Rate Versus ◽  
Public Data

Abstract This paper presents a method to identify switch time from end of linear flow (telf) to transition or boundary-dominated flow (BDF) by utilizing multiple diagnostic plots including a Modified Fetkovich type curve (Eleiott et al. 2019). In this study, we analyzed publicly available production data to analyze transient linear flow behavior and boundary-dominated flow from multiple unconventional reservoirs. This method applies a log-log plot of rate versus time combined with a log-log plot of rate versus material balance time (MBT). In addition to log-log plots, a specialized plot of rate versus square root of time is used to confirm telf. A plot of MBT versus actual time, t, is provided to convert material balance time to actual time, and vice versa. The Modified Fetkovich type curve is used to estimate decline parameters and reservoir properties. Applications of this method using monthly production data from Bakken and Permian Shale areas are presented in this work. Utilizing public data, our comprehensive review of approximately 800 multi-staged fractured horizontal wells (MFHW) from North American unconventional reservoirs found many of them exhibiting linear flow production characteristics. To identify end of linear flow, a log-log plot of rate versus time alone is not sufficient, especially when a well is not operated in a consistent manner. This paper shows using additional diagnostic plots such as rate versus MBT and specialized plots can assist interpreters to better identify end of linear flow. With the end of linear flow determined for these wells, the interpreter can use the telf to forecast future production and estimate reservoir properties using the modified type curve. These diagnostic plots can be added to existing production analysis tools so that engineers can analyze changes in flow regimes in a timely manner, have better understanding of how to forecast their wells, and reduce the uncertainty in estimated ultimate recoveries related to decline parameters.

Analytical Rate-Transient Analysis and Production Performance of Waterflooded Fields with Delayed Injection Support

2021 ◽  
pp. 1-23
Author(s):  
Daniel O'Reilly ◽  
Manouchehr Haghighi ◽  
Mohammad Sayyafzadeh ◽  
Matthew Flett
Keyword(s):  
Steady State ◽  
Transient Analysis ◽  
Water Injection ◽  
Data Interpretation ◽  
Production Performance ◽  
Production Data ◽  
Reservoir Properties ◽  
Two Phase ◽  
Production Forecasting ◽  
Rate Transient Analysis

Summary An approach to the analysis of production data from waterflooded oil fields is proposed in this paper. The method builds on the established techniques of rate-transient analysis (RTA) and extends the analysis period to include the transient- and steady-state effects caused by a water-injection well. This includes the initial rate transient during primary production, the depletion period of boundary-dominated flow (BDF), a transient period after injection starts and diffuses across the reservoir, and the steady-state production that follows. RTA will be applied to immiscible displacement using a graph that can be used to ascertain reservoir properties and evaluate performance aspects of the waterflood. The developed solutions can also be used for accurate and rapid forecasting of all production transience and boundary-dominated behavior at all stages of field life. Rigorous solutions are derived for the transient unit mobility displacement of a reservoir fluid, and for both constant-rate-injection and constant-pressure-injection after a period of reservoir depletion. A simple treatment of two-phase flow is given to extend this to the water/oil-displacement problem. The solutions are analytical and are validated using reservoir simulation and applied to field cases. Individual wells or total fields can be studied with this technique; several examples of both will be given. Practical cases are given for use of the new theory. The equations can be applied to production-data interpretation, production forecasting, injection-water allocation, and for the diagnosis of waterflood-performanceproblems. Correction Note: The y-axis of Fig. 8d was corrected to "Dimensionless Decline Rate Integral, qDdi". No other content was changed.

Analysis of time-lapse seismic and production data for reservoir model classification and assessment

2018 ◽  
Vol 15 (4) ◽  
pp. 1561-1587 ◽  
Author(s):  
Rafael Souza ◽  
David Lumley ◽  
Jeffrey Shragge ◽  
Alessandra Davolio ◽  
Denis José Schiozer
Keyword(s):  
Time Lapse ◽  
Production Data ◽  
Reservoir Model ◽  
Model Classification
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