Stochastic inverse modeling and global sensitivity analysis to assist interpretation of drilling mud losses in fractured formations

Russian, Anna; Riva, Mònica; Russo, E.; Chiaramonte, M. A.; Guadagnini, Alberto

doi:10.1007/s00477-019-01729-4

Cited by 10 publications

(13 citation statements)

References 33 publications

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“…In the knowledge of the data covariance matrix including the data variances in its main diagonal, one can derive the covariance matrix of the estimated model parameters using linear inverse theory (Menke 1984). The PSO method as intelligent optimization tool has been implemented in a stochastic calibration context, specifically for the estimation of subsurface properties (Russian et al 2019;Patani et al 2021). Its efficiency has been shown in some applications in well logging inversion, too.…”

Section: Discussionmentioning

confidence: 99%

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

2022

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In this paper, we present an innovative factor analysis algorithm for hydrocarbon exploration to estimate the intrinsic permeability of reservoir rocks from well logs. Unlike conventional evaluation methods that employ a single or a limited number of data types, we process simultaneously all available data to derive the first statistical factor and relate it to permeability by regression analysis. For solving the problem of factor analysis, we introduce an improved particle swarm optimization method, which searches for the global minimum of the distance between the observed and calculated data and gives a quick estimation for the factor scores. The learning factors of the intelligent computational technique such as the cognitive and social constants are specified as hyperparameters and calculated by using simulated annealing algorithm as heuristic hyperparameter estimator. Instead of the arbitrary fixation of these hyperparameters, we refine them in an iterative process to give reliable estimation both for the statistical factors and formation permeability. The estimated learning parameters are consistent with literature recommendations. We demonstrate the feasibility of the proposed well-log analysis method by a Hungarian oilfield study involving open-hole wireline logs and core data. We determine the spatial distribution of permeability both along a borehole and between more wells using the factor analysis approach, which serves as efficient and reliable multivariate statistical tool for advanced formation evaluation and reservoir modeling.

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Section: Discussionmentioning

confidence: 99%

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

2022

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“…[2012], Russian et al. [2019] and references therein). In the PSO approach, a set of particles are initially spread randomly across the parameter space, each with an initial random velocity.…”

Section: Methodsmentioning

confidence: 99%

“…We implement a stochastic inverse modeling workflow by relying on the particle swarm optimization (PSO) machine-learning technique (e.g., Robinson & Rahmat-Samii, 2004). The latter is based on a stochastic evolutionary algorithm that has been shown to be conducive to robust results and with acceptable computational costs in the context of several environmental applications associated with a high number of parameters (see, e.g., Castagna and Bellin [2009], Majone et al [2012], Russian et al [2019] and references therein). In the PSO approach, a set of particles are initially spread randomly across the parameter space, each with an initial random velocity.…”

Section: Stochastic Model Calibrationmentioning

confidence: 99%

Uncertainty Analysis and Identification of Key Parameters Controlling Bacteria Transport Within a Riverbank Filtration Scenario

Knabe

Guadagnini

Riva

et al. 2021

Water Resources Research

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Managed aquifer recharge using bank filtration is an important approach to produce sustainable drinking water in regions with limited groundwater resources. Gillefalk et al. (2018) provide an overview of the global usage of induced river bank filtration (RBF). For example, RBF provides about 60% of the total drinking water demand in the city of Berlin (Germany, 3.6 Mio. inhabitants), while it is used to serve almost 100% of the population of Düsseldorf (Germany, 0.6 Mio inhabitants) with drinking water (Gillefalk et al., 2018). The RBF concept is based on natural remediation (or attenuation), that is, on the processes occurring in groundwater (such as dispersion and biodegradation) lowering contaminant concentrations, thus reducing the need for additional treatments to obtain drinking water quality.Pathogens, for example, viruses or some bacteria, are mainly released to the environment by wastewater discharge into streams (Xagoraraki et al., 2014). Lakes and rivers around big cities or near large urban areas are often documented to be severely polluted by pathogens, resulting in a high risk of pathogen contamination of groundwater, including RBF sites. Pathogens in drinking water can cause a variety of diseases in humans. The number of pathogen particles which are necessary to cause an infection (also termed infective dose) can vary between a few particles to thousands of particles, depending on the pathogen species (Schmid-Hempel & Frank, 2007). Accurate assessment of pathogen fate and transport are especially relevant for pathogens with a low infective dose. Bacteria concentrations serve as primary indicators for drinking water quality, as set in the World Health Organization guidelines (WHO, 2017) and regulations in several countries (e.g., EU Drinking Water Directive 98/83/EC). Measured bacteria are either pathogens (e.g., some Escherichia coli strains, or the genus Enterococcus) or pathogen/wastewater indicators (e.g., coliforms).

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“…In this context, the ensuing estimates of equivalent fracture aperture correspond to a parallel plate system whose hydraulic behavior is equivalent to the one observed through monitored fluid loss rates. Thus, the concept can imbue the effect of a single fracture or of a network of (possibly micro-)fractures whose presence is imprinted onto the pattern of the observed fluid losses (e.g., Verga et al 2000;Russian et al 2019). The analysis workflow relies on the real-time information associated with temporal histories of drilling mud losses and on the use of a simple analytical formulation.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of equivalent fracture aperture constrained on quasi-real-time drilling mud loss data

Patani

Colombo²,

Russo³

et al. 2022

J Petrol Explor Prod Technol

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We provide a rigorous workflow to quantify the effects of key sources of uncertainty associated with equivalent fracture aperture estimates w constrained through mud loss information acquired while drilling a well in a reservoir. A stochastic inverse modeling framework is employed to estimate the probability distribution of w. This choice is consistent with the quantity and quality of available data. The approach allows assessing the probability that values of w inferred from mud loss events exceed a given threshold. We rely on a streamlined analytical solution to model mud losses while drilling. We explicitly consider uncertainties associated with model parameters and forcing terms, including drilling fluid rheological properties and flow rates, pore fluid pressure, and dynamic drilling fluid pressure. A synthetic scenario is considered to provide a transparent reference setting against which our stochastic inverse modeling workflow can be appraised. The approach is then applied to a real-case scenario. The latter is associated with data monitored on a rig site. A direct comparison of the impact of data collected through two common techniques (respectively, relying on flow meter sensors or pump strokes) on the ensuing probability of w is provided. A detailed analysis of the uncertainty related to the level of data corruption is also performed, considering various levels of measurement errors. Results associated with the field setting suggest that the proposed workflow yields probability distribution of w that are compatible with interpretations relying on traditional analyses of image logs. Results stemming from direct and indirect flow data display similar shapes. This suggests the viability of the probabilistic inversion methodology to assist quasi-real-time identification of equivalent fracture apertures on the basis of routinely acquired information during drilling.

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Stochastic inverse modeling and global sensitivity analysis to assist interpretation of drilling mud losses in fractured formations

Cited by 10 publications

References 33 publications

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

Uncertainty Analysis and Identification of Key Parameters Controlling Bacteria Transport Within a Riverbank Filtration Scenario

Probabilistic assessment of equivalent fracture aperture constrained on quasi-real-time drilling mud loss data

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