Risk quantification combining geostatistical realizations and discretized Latin Hypercube

Schiozer, Denis José; Avansi, Guilherme Daniel; Santos, Antonio Alberto de Souza dos

doi:10.1007/s40430-016-0576-9

Cited by 61 publications

(39 citation statements)

References 39 publications

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“…We use DLHC because it ensures that values over the whole range of a model attribute are selected. Schiozer et al (2015) used the DLHC to combine simulation model attributes and geostatistical images (which they called Discretized Latin Hypercube combined with Geostatistical realizations -DLHG). They first generated n images and combined these with the attributes of the simulation model.…”

Section: Sampling and Generation Of Modelsmentioning

confidence: 99%

“…The geostatistical simulation then generates one realization of the discrete fracture network, which is upscaled to generate the fracture spacing and permeability. For more details on DLHC, see Maschio and Schiozer (2016), and Schiozer et al (2015).…”

Section: Sampling and Generation Of Modelsmentioning

confidence: 99%

“…A common concern for reservoir simulation is the number of simulations. Schiozer et al (2015) suggested 100-300 to be a good number for risk quantification in their study. For this case, we ran tests and concluded that 500 simulations provide an acceptable range of variability of NQDS.…”

Section: Parameterization For the Uncertainty Reductionmentioning

confidence: 99%

See 2 more Smart Citations

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Costa

Maschio

Schiozer

2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir.

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Section: Sampling and Generation Of Modelsmentioning

confidence: 99%

Section: Sampling and Generation Of Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Costa

Maschio

Schiozer

2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…However, as the sampling is purely random, a very high number of samples is necessary to ensure reliable results (Mishra, 1998), frequently at unfeasible levels (Risso et al, 2011). This study uses a simplified statistical technique developed in a related work (Schiozer et al, 2017), the Discretized Latin Hypercube with Geostatistical realizations (DLHG). By incorporating the desirable features of random sampling and stratified sampling, the DLHG ensures minimum computational costs without requiring proxy models.…”

Section: Introductionmentioning

confidence: 99%

“…We tested this technique in several examples, achieving a good balance of precision and computational time. This sampling technique was applied to uncertainty quantification (Schiozer et al, 2017), history matching (Maschio and Schiozer, 2016), and production strategy optimization (von Hohendorff Filho et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Model-based decision analysis applied to petroleum field development and management

Schiozer

Santos

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

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Estimation of the chance of success of a four-dimensional seismic project for a developed oil field

et al. 2022

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Risk quantification combining geostatistical realizations and discretized Latin Hypercube

Cited by 61 publications

References 39 publications

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Model-based decision analysis applied to petroleum field development and management

Estimation of the chance of success of a four-dimensional seismic project for a developed oil field

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