pynoddy 1.0: an experimental platform for automated 3-D  kinematic and potential field modelling

Wellmann, Florian; Thiele, Samuel T.; Lindsay, Mark; Jessell, Mark

doi:10.5194/gmd-9-1019-2016

Cited by 29 publications

(15 citation statements)

References 41 publications

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“…This avoids timeconsuming mechanical simulation where physical parameters can be difficult to choose. As a result, the presented fault operator can be run interactively and could be useful to sketch three-dimensional models and tectonic history (Jessell, 1981;Jackson et al, 2015;Wellmann et al, 2015;Laurent et al, 2015;Godefroy et al, 2017). Our workflow could also be used to assess structural and kinematic uncertainties by looking not only for the best solution to the optimization but also for a set of acceptable solutions (Cardozo et al, 2011), opening the way to a multi-scenario analysis of fault-kinematics and fault-seal properties.…”

Section: Discussionmentioning

confidence: 99%

A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data

et al. 2018

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Fault-related displacements impact oil and gas flow predictions at reservoir scales. In this contribution, we integrate a quantitative description of fault-related deformation directly embedded into the structural modeling workflow. Consistent fault displacements are produced by using numerical fault operators that deform horizons in accordance with theoretical isolated fault displacement models to generate kinematically consistent structural models.We compare structural modeling approaches based on such fault operators with those relying on interpolation. Several synthetic cross-sections are generated from a reference high-resolution structural model of the Santos Basin, Brazil. Models are reconstructed from this 2D synthetic sparse dataset using both methods. Their ability to produce consistent structural models is assessed by comparing reconstructed and reference models. On this example, kinematic modeling improves the quality of automatically generated models when only few and poor quality observations are available, thus reducing the time needed for structural validation.

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Section: Discussionmentioning

confidence: 99%

A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data

et al. 2018

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“…Uncertainty quantification and its logical extension into probabilistic machine learning will not be covered in the depth in this paper due to the broad scope of the subject. However, the main goal of GemPy is to serve as main generative model within these probabilistic approaches and as such we will provide a demonstration of how GemPy fits on the workflow of our previous work (de la Varga and Wellmann, 2016;Wellmann et al, 2017) as well as how this work may set the foundations for an easier expansion into the domain of probabilistic machine learning in the future.…”

Section: Stochastic Geomodeling and Probabilistic Programmingmentioning

confidence: 99%

“…Although computing the forward gravity has its own value for many applications, the main aim of GemPy is to integrate all possible sources of information into a single probabilistic framework. The use of likelihood functions in a Bayesian inference in comparison to simple forward simulation has been explored by the authors during recent years (de la Varga and Wellmann et al, 2017;Schaaf, 2017). This approach enables us to tune the conditioning of possible stochastic realizations by varying the probabilistic density function used as likelihoods.…”

Section: Geological Inversion: Gravity and Topologymentioning

confidence: 99%

GemPy 1.0: open-source stochastic geological modeling and inversion

2019

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Abstract. The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications, ranging from geofluid reservoir studies, over raw material investigations, to geosequestration, as well as many branches of geoscientific research and applications in geological surveys. A wide range of methods exist to generate geological models. However, the powerful methods are behind a paywall in expensive commercial packages. We present here a full open-source geomodeling method, based on an implicit potential-field interpolation approach. The interpolation algorithm is comparable to implementations in commercial packages and capable of constructing complex full 3-D geological models, including fault networks, fault–surface interactions, unconformities and dome structures. This algorithm is implemented in the programming language Python, making use of a highly efficient underlying library for efficient code generation (Theano) that enables a direct execution on GPUs. The functionality can be separated into the core aspects required to generate 3-D geological models and additional assets for advanced scientific investigations. These assets provide the full power behind our approach, as they enable the link to machine-learning and Bayesian inference frameworks and thus a path to stochastic geological modeling and inversions. In addition, we provide methods to analyze model topology and to compute gravity fields on the basis of the geological models and assigned density values. In summary, we provide a basis for open scientific research using geological models, with the aim to foster reproducible research in the field of geomodeling.

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“…The MCUE approach is usually applied to geometric modeling engines (Wellmann and Regenauer-Lieb 2012;Lindsay et al 5 2013;Jessell et al 2014a;Jessell et al 2010), although it can be applied to dynamic or kinematic engines (Wang et al 2016;Wellmann et al 2015). This choice is motivated by critical differences between the three approaches, both at the conceptual and practical level (Aug 2004).…”

Section: Mcue Methodsmentioning

confidence: 99%

Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

Pakyuz-Charrier¹,

Lindsay²,

Ogarko

et al. 2017

Preprint

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Abstract. Three-dimensional (3D) geological modeling aims to determine geological information in a 3D space using structural data (foliations and interfaces) and topological rules as inputs. They are necessary in any project where the properties of the subsurface matters, they express our understanding of geometries in depth. For that reason, 3D geological models have a wide range of practical applications including but not restrained to civil engineering, oil and gas industry, mining industry 10 and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) combined with input uncertainty (observational-, conceptual-and technical errors). Because 3D geological models are often used for impactful decision making it is critical that all 3D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data uncertainty propagation in implicit 3D geological modeling using GeoModeller API. This aim is achieved using Monte 15Carlo simulation uncertainty estimation (MCUE), a heuristic stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution 20 selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of inappropriate disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e. scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate 25 that the proposed workflows improve the reliability of uncertainty estimation and diminishes the occurrence of artefacts.Solid Earth Discuss., https://doi

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pynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modelling

Cited by 29 publications

References 41 publications

A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data

A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data

GemPy 1.0: open-source stochastic geological modeling and inversion

Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

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