Showing papers in "Geophysics in 2017"

A scalable deep learning platform for identifying geologic features from seismic attributes

Abstract: The modern requirement for analyzing and interpreting ever-larger volumes of seismic data to identify prospective hydrocarbon prospects within stringent time deadlines represents an ongoing challenge in petroleum exploration. To provide a computer-based aid in addressing this challenge, we have developed a “big data” platform to facilitate the work of geophysicists in interpreting and analyzing large volumes of seismic data with scalable performance. We have constructed this platform on a modern distributed-memory infrastructure, providing a customized seismic analytics software development toolkit, and a Web-based graphical workflow interface along with a remote 3D visualization capability. These support the management of seismic data volumes, attributes processing, seismic analytics model development, workflow execution, and 3D volume visualization on a scalable, distributed computing platform. Early experiences show that computationally demanding deep learning methods such as convolutional neural netwo...

Automated fault detection without seismic processing

Abstract: For hydrocarbon exploration, large volumes of data are acquired and used in physical modeling-based workflows to identify geologic features of interest such as fault networks, salt bodies, or, in general, elements of petroleum systems. The adjoint modeling step, which transforms the data into the model space, and subsequent interpretation can be very expensive, both in terms of computing resources and domain-expert time. We propose and implement a unique approach that bypasses these demanding steps, directly assisting interpretation. We do this by training a deep neural network to learn a mapping relationship between the data space and the final output (particularly, spatial points indicating fault presence). The key to obtaining accurate predictions is the use of the Wasserstein loss function, which properly handles the structured output — in our case, by exploiting fault surface continuity. The promising results shown here for synthetic data demonstrate a new way of using seismic data and suggest more d...

Hydraulic-fracture geometry characterization using low-frequency DAS signal

Abstract: Monitoring and diagnosing completion during hydraulic-fracturing operations provides insight into the fracture geometry, interwell frac hits, and connectivity. Conventional monitoring methods (microseismic, pressure gauges, tracers, etc.) can provide a range of information about the stimulated rock volume but may often be limited in detail or clouded by uncertainty. Utilization of distributed acoustic sensing (DAS) as a fracture monitoring tool is growing; however, most of the applications have been limited to acoustic frequency bands of the DAS recorded signal. In this paper, we demonstrate some examples of using the low-frequency band of DAS signal to constrain hydraulic-fracture geometry. DAS data were acquired in both offset horizontal and vertical monitor wells. In horizontal wells, DAS data record formation strain perturbation due to fracture propagation. Events like fracture opening and closing, stress shadow creation and relaxation, ball seat, and plug isolation can be clearly identified....

Directional structure-tensor-based coherence to detect seismic faults and channels

Abstract: Seismic coherence is widely used in seismic interpretation and reservoir characterization to highlight (with low values) faults and stratigraphic features from a seismic image. A coherence image can be computed from the eigenvalues of conventional structure tenors, which are outer products of gradients of a seismic image. I have developed a simple but effective method to improve such a coherence image by using directional structure tensors, which are different from the conventional structure tensors in only two aspects. First, instead of using image gradients with vertical and horizontal derivatives, I use directional derivatives, computed in directions perpendicular and parallel to seismic structures (reflectors), to construct directional structure tensors. With these directional derivatives, lateral seismic discontinuities, especially those subtle stratigraphic features aligned within dipping structures, can be better captured in the structure tensors. Second, instead of applying Gaussian smooth...

What can machine learning do for seismic data processing? An interpolation application

Abstract: Machine learning (ML) systems can automatically mine data sets for hidden features or relationships. Recently, ML methods have become increasingly used within many scientific fields. We have evaluated common applications of ML, and then we developed a novel method based on the classic ML method of support vector regression (SVR) for reconstructing seismic data from under-sampled or missing traces. First, the SVR method mines a continuous regression hyperplane from training data that indicates the hidden relationship between input data with missing traces and output completed data, and then it interpolates missing seismic traces for other input data by using the learned hyperplane. The key idea of our new ML method is significantly different from that of many previous interpolation methods. Our method depends on the characteristics of the training data, rather than the assumptions of linear events, sparsity, or low rank. Therefore, it can break out the previous assumptions or constraints and show u...

Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

Abstract: Recorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on s...

Vector-based elastic reverse time migration based on scalar imaging condition

Abstract: The scalar images (PP, PS, SP, and SS) of elastic reverse time migration (ERTM) can be generated by applying an imaging condition as crosscorrelation of pure wave modes. In conventional ERTM, Helmholtz decomposition is commonly applied in wavefield separation, which leads to a polarity reversal problem in converted-wave images because of the opposite polarity distributions of the S-wavefields. Polarity reversal of the converted-wave image will cause destructive interference when stacking over multiple shots. Besides, in the 3D case, the curl calculation generates a vector S-wave, which makes it impossible to produce scalar PS, SP, and SS images with the crosscorrelation imaging condition. We evaluate a vector-based ERTM (VB-ERTM) method to address these problems. In VB-ERTM, an amplitude-preserved wavefield separation method based on decoupled elastic wave equation is exploited to obtain the pure wave modes. The output separated wavefields are both vectorial. To obtain the scalar images, the scala...

Elastic least-squares reverse time migration

Abstract: We use elastic least-squares reverse time migration (LSRTM) to invert for the reflectivity images of P- and S-wave impedances. Elastic LSRTM solves the linearized elastic-wave equations for forward modeling and the adjoint equations for backpropagating the residual wavefield at each iteration. Numerical tests on synthetic data and field data reveal the advantages of elastic LSRTM over elastic reverse time migration (RTM) and acoustic LSRTM. For our examples, the elastic LSRTM images have better resolution and amplitude balancing, fewer artifacts, and less crosstalk compared with the elastic RTM images. The images are also better focused and have better reflector continuity for steeply dipping events compared to the acoustic LSRTM images. Similar to conventional least-squares migration, elastic LSRTM also requires an accurate estimation of the P- and S-wave migration velocity models. However, the problem remains that, when there are moderate errors in the velocity model and strong multiples, LSRTM ...

Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data

Abstract: Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing ...

Transdimensional seismic inversion using the reversible jump Hamiltonian Monte Carlo algorithm

Abstract: Prestack or angle stack gathers are inverted to estimate pseudologs at every surface location for building reservoir models. Recently, several methods have been proposed to increase the resolution of the inverted models. All of these methods, however, require that the total number of model parameters be fixed a priori. We have investigated an alternate approach in which we allow the data themselves to choose model parameterization. In other words, in addition to the layer properties, the number of layers is also treated as a variable in our formulation. Such transdimensional inverse problems are generally solved by using the reversible jump Markov chain Monte Carlo (RJMCMC) approach, which is a tool for model exploration and uncertainty quantification. This method, however, has very low acceptance. We have developed a two-step method by combining RJMCMC with a fixed-dimensional MCMC called Hamiltonian Monte Carlo, which makes use of gradient information to take large steps. Acceptance probability ...

Rayleigh-Marchenko redatuming for target-oriented, true-amplitude imaging

Abstract: Marchenko redatuming is a revolutionary technique to estimate Green’s functions from virtual sources in the subsurface using only data measured at the earth’s surface, without having to place either sources or receivers in the subsurface. This goal is achieved by crafting special wavefields (so-called focusing functions) that can focus energy at a chosen point in the subsurface. Despite its great potential, strict requirements on the reflection response such as knowledge and accurate deconvolution of the source wavelet (including absolute scaling factor) and co-location of sources and receivers have so far challenged the application of Marchenko redatuming to real-world scenarios. I combine the coupled Marchenko equations with a one-way version of the Rayleigh integral representation to obtain a new redatuming scheme that handles internal as well as free-surface multiples using dual-sensor, band-limited seismic data (with an unknown source signature) from any acquisition system that presents arbit...

Analysis of signal to noise and directivity characteristics of das VSP at near and far offsets-A CO2CRC Otway Project data example

Abstract: During the last decade, distributed acoustic sensing (DAS) has emerged as a new technology for seismic acquisition. DAS has the potential to reduce the cost of permanent monitoring operations over time as it offers long equipment survivability and requires minimum maintenance. However, broad adoption of DAS technology still faces some challenges, such as low sensitivity and high levels of noise compared to conventional seismic sensors. Recent developments in fiber-optic systems and cable designs aim to overcome these limitations. To understand how DAS can be used in monitoring applications, it is important to know how it behaves with varying offsets and incidence angles. An offset VSP survey was acquired, at the CO2CRC Otway Project, using a straight single-mode fiber, a straight “enhanced-backscatter” fiber, and a conventional three-component geophone tool. The results from this survey show that DAS has the potential to provide similar, or even superior, quality data sets as conventional geophones.

The potential of rotary-wing UAV-based magnetic surveys for mineral exploration: A case study from central Sweden

Abstract: Unmanned aerial vehicle (UAV)-based geophysical surveys are attractive for land mineral exploration and are gradually opening extraordinary opportunities in providing high-resolution definition of geologic structures and for direct targeting of mineral deposits. There are, however, challenges such as electromagnetic noise from the UAV, limited load capacity, and short flight times. If these are overcome, there will be a new era in using UAV-based geophysical systems for mineral exploration and for a number of mining-related purposes. In this study, we have tested the potential of rotary-wing UAV systems, given their flight flexibility and robustness for direct targeting of iron-oxide deposits in central Sweden. A walking-mode high-precision Overhauser magnetometer was reassembled so that it could be lifted by the rotary-wing system. Successful backyard tests were performed, but during the real experiment several issues related to high UAV noise level and extreme magnetic field from the mineraliza...

Preserving the discontinuities in least-squares reverse time migration of simultaneous-source data

Abstract: The simultaneous-source shooting technique can accelerate field acquisition and improve spatial sampling but it will cause strong interferences in the recorded data and artifacts in the final image. The previously proposed structural smoothing operator can effectively attenuate artifacts for relatively simple reflection structures during least-squares inversion, but it will cause damage to complicated reflection events such as discontinuities. To preserve discontinuities in a seismic image, we apply the singular spectrum analysis (SSA) operator to attenuate artifacts during least-squares inversion. Considering that global SSA cannot deal with overcomplicated data very well, we use local SSA to remove noise and to better preserve the steeply dipping components. The local SSA operator corresponds to a local low-rank constraint applied in the inversion process. The migration operator used in the study is the reverse time migration (RTM) operator. Tests using the Marmousi model showed the superior per...

Signal extraction using randomized-order multichannel singular spectrum analysis

Abstract: Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. Th...

Elastic wavefield separation based on the Helmholtz decomposition

Abstract: Divergence and curl operators used for the decomposition of P- and S-wave modes in elastic reverse time migration (RTM) change the amplitudes, units, and phases of extrapolated wavefields. I separate the P- and S-waves in elastic media based on the Helmholtz decomposition. The decomposed wavefields based on this approach have the same amplitudes, units, and phases as the extrapolated wavefields. To avoid expensive multidimensional integrals in the Helmholtz decomposition, I introduce a fast Poisson solver to efficiently solve the vector Poisson’s equation. This fast algorithm allows us to reduce computational complexity from O(N2) to O(N log N), where N is the total number of grid points. Because the decomposed P- and S-waves are vector fields, I use vector imaging conditions to construct PP-, PS-, SS-, and SP-images. Several 2D numerical examples demonstrate that this approach allows us to accurately and efficiently decompose P- and S-waves in elastic media. In addition, elastic RTM images based ...

Hybrid rank-sparsity constraint model for simultaneous reconstruction and denoising of 3D seismic data

Abstract: We have determined an approach for simultaneous reconstruction and denoising of 3D seismic data with randomly missing traces. The core in simultaneous reconstruction and denoising of 3D seismic data is the choice of constraint method. Recently, there have been two types of popular approaches to choose such a constraint: sparsity-promoting transforms using a sparsity constraint and rank reduction methods using a rank constraint. Although the sparsity-promoting transform enjoys the direct advantage of high efficiency, it lacks adaptivity to a variety of data patterns. On the other hand, the rank reduction method can be adaptively applied to different data sets, but its computational cost is quite high. We investigate multiple constraints for simultaneous seismic data reconstruction and denoising based on a novel hybrid rank-sparsity constraint (HRSC) model, which aims at combining the benefits of the sparsity-promoting transforms and rank reduction methods. Also, we design the corresponding HRSC alg...

Horizontal resolution of multichannel analysis of surface waves

Abstract: The multichannel analysis of surface wave (MASW) method has been effectively and widely used to determine near-surface shear-wave velocity. Horizontal resolution of the MASW method represents the minimum horizontal length of recognizable geologic anomalous bodies on a pseudo-2D S-wave velocity VS section. Accurately assessing the achievable lateral resolution is one of the main issues in lateral variation reconstruction using the MASW method. It is difficult to quantitatively estimate the horizontal resolution of the MASW method because of the many influencing factors, such as parameters of the observation system, the depth of an anomalous body, and the velocity contrast between the anomalous body and the surrounding rocks. We first analyzed the horizontal resolution of the MASW method based on numerical simulation experiments. According to different influencing factors of the horizontal resolution, we established different laterally heterogeneous models and observation systems and then simulated ...

Application of a complete workflow for 2D elastic full-waveform inversion to recorded shallow-seismic Rayleigh waves

Abstract: The S-wave velocity of the shallow subsurface can be inferred from shallow-seismic Rayleigh waves. Traditionally, the dispersion curves of the Rayleigh waves are inverted to obtain the (local) S-wave velocity as a function of depth. Two-dimensional elastic full-waveform inversion (FWI) has the potential to also infer lateral variations. We have developed a novel workflow for the application of 2D elastic FWI to recorded surface waves. During the preprocessing, we apply a line-source simulation (spreading correction) and perform an a priori estimation of the attenuation of waves. The iterative multiscale 2D elastic FWI workflow consists of the preconditioning of the gradients in the vicinity of the sources and a source-wavelet correction. The misfit is defined by the least-squares norm of normalized wavefields. We apply our workflow to a field data set that has been acquired on a predominantly depth-dependent velocity structure, and we compare the reconstructed S-wave velocity model with the result...

Seismic monitoring leveraging existing telecom infrastructure at the SDASA: Active, passive, and ambient-noise analysis

Abstract: We analyze active and passive seismic data recorded by the Stanford distributed acoustic sensing array (SDASA) located in conduits under the Stanford University campus. For the active data we used low-energy sources (betsy gun and sledge hammer) and recorded data using both the DAS array and 98 three-component nodes deployed along a 2D line. The joint analysis of shot profiles extracted from the two data sets shows that some surface waves and refracted events are consistently recorded by the DAS array. In areas where geophone coupling was suboptimal because of surface obstructions, DAS recordings are more coherent. In contrast, surface waves are more reliably recorded by the geophones than the DAS array. Because of the noisy environment and weak sources, neither data set shows clear reflections. We demonstrate the repeatability of DAS recordings of local earthquakes by comparing two weak events (magnitude 0.95 and 1.34) with epicenters 100 m apart that occurred only one minute from each other. An...

Implementation of the Marchenko method

Abstract: The Marchenko method makes it possible to compute subsurface-to-surface Green's functions from reflection measurements at the surface. Applications of the Marchenko method have already been discussed in many papers, but its implementation aspects have not yet been discussed in detail. Solving the Marchenko equation is an inverse problem. The Marchenko method computes a solution of the Marchenko equation by an (adaptive) iterative scheme or by a direct inversion. We have evaluated the iterative implementation based on a Neumann series, which is considered to be the conventional scheme. At each iteration of this scheme, a convolution in time and an integration in space are performed between a so-called focusing (update) function and the reflection response. In addition, by applying a time window, one obtains an update, which becomes the input for the next iteration. In each iteration, upgoing and downgoing focusing functions are updated with these terms. After convergence of the scheme, the resulting upgoing and downgoing focusing functions are used to compute the upgoing and downgoing Green's functions with a virtual-source position in the subsurface and receivers at the surface. We have evaluated this algorithm in detail and developed an implementation that reproduces our examples. The software fits into the Seismic Unix software suite of the Colorado School of Mines.

Elastic reflection-based waveform inversion with a nonlinear approach

Abstract: Full-waveform inversion (FWI) is a highly nonlinear problem due to the complex reflectivity of the earth, and this nonlinearity only increases under the more expensive elastic assumption. In elastic media, we need a good initial P-wave velocity and even better initial S-wave velocity models with accurate representation of the low model wavenumbers for FWI to converge. However, inverting for the low-wavenumber components of P- and S-wave velocities using reflection waveform inversion (RWI) with an objective to fit the reflection shape, rather than produce reflections, may mitigate the limitations of FWI. Because FWI, performing as a migration operator, is preferred of the high-wavenumber updates along reflectors. We have developed an elastic RWI that inverts for the low-wavenumber and perturbation components of the P- and S-wave velocities. To generate the full elastic reflection wavefields, we derive an equivalent stress source made up by the inverted model perturbations and incident wavefields. W...

Fast-Fourier-based error propagation for the gravimetric terrain correction

Abstract: We have identified a gap in the literature on error propagation in the gravimetric terrain correction. Therefore, we have derived a mathematical framework to model the propagation of spatially correlated digital elevation model errors into gravimetric terrain corrections. As an example, we have determined how such an error model can be formulated for the planar terrain correction and then be evaluated efficiently using the 2D Fourier transform. We have computed 18.3 billion linear terrain corrections and corresponding error estimates for a 1 arc-second (∼30 m) digital elevation model covering the whole of the Australian continent.

Accounting for free-surface multiples in Marchenko imaging

Abstract: Imagine placing a receiver at any location in the earth and recording the response at that location to sources on the surface. In such a world, we could place receivers around our reservoir to better image the reservoir and understand its properties. Realistically, this is not a feasible approach for understanding the subsurface. We have developed an alternative and realizable approach to obtain the response of a buried virtual receiver for sources at the surface. Our method is capable of retrieving the Green’s function for a virtual point in the subsurface to the acquisition surface. In our case, a physical receiver is not required at the subsurface point; instead, we require the reflection measurements for sources and receivers at the surface of the earth and a macromodel of the velocity (no small-scale details of the model are necessary). We can interpret the retrieved Green’s function as the response to sources at the surface for a virtual receiver in the subsurface. We obtain this Green’s function by solving the Marchenko equation, an integral equation pertinent to inverse scattering problems. Our derivation of the Marchenko equation for the Green’s function retrieval takes into account the free-surface reflections present in the reflection response (previous work considered a response without free-surface multiples). We decompose the Marchenko equation into up- and downgoing fields and solve for these fields iteratively. The retrieved Green’s function not only includes primaries and internal multiples as do previous methods, but it also includes freesurface multiples. We use these up- and downgoing fields to obtain a 2D image of our area of interest, in this case, below a synclinal structure.

4D DAS VSP as a tool for frequent seismic monitoring in deep water

Abstract: Thanks to its relatively low cost, time-lapse vertical seismic profiling (VSP) with distributed acoustic sensing (DAS) is an attractive proposition for frequent seismic monitoring around production and injection wells, aimed at production optimization in complex deepwater fields. We demonstrate that 4D DAS VSP provides sufficient data quality for reservoir monitoring in deep water, with a high value-to-investment ratio. 4D quality and image extent can be enhanced through well-executed acquisition and advanced processing. Further cost lowering via innovations on the source side is being pursued. The path to establishing 4D DAS VSP as a monitoring tool in deep water is through further field applications to demonstrate its ability to impact business decisions.

Mathematical morphological filtering for linear noise attenuation of seismic data

Abstract: Linear coherent noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use the differences in frequency, wavenumber, or amplitude to separate the useful signal and coherent noise. However, the application of traditional methods is limited or even invalid when the aforementioned differences between useful signal and coherent noise are too small to be distinguished. For this reason, we have managed to develop a new algorithm from the differences in the shape of seismic waves, and thus, introduce mathematical morphological filtering (MMF) into coherent noise attenuation. The morphological operation is calculated in the trace direction of a rotating coordinate system. This rotating coordinate system is along the direction of the trajectory of coherent noise to make the energy of the coherent noise distributed along the horizontal direction. The MMF approach is more effective than mean and median filters in rejecting abnormal values and causes fe...

On the role of multiples in Marchenko imaging

Abstract: Marchenko imaging can produce seismic reflection images in which artifacts related to multiples are suppressed. However, in state-of-the-art implementations, multiples do not contribute to the imaged reflectors. With an “event-by-event” deconvolution imaging approach, it is possible to use multiples in Marchenko imaging. We illustrate this for a 1D reflection response in which the primary reflection of a specific interface is missing.

Elastic least-squares reverse time migration via linearized elastic full-waveform inversion with pseudo-Hessian preconditioning

Abstract: Time-domain elastic least-squares reverse time migration (LSRTM) is formulated as a linearized elastic full-waveform inversion problem. The elastic Born approximation and elastic reverse time migration (RTM) operators are derived from the time-domain continuous adjoint-state method. Our approach defines P- and S-wave impedance perturbations as unknown elastic images. Our algorithm is obtained using continuous functional analysis in which the problem is discretized at the final stage (optimize-before-discretize approach). The discretized numerical versions of the elastic Born operator and its adjoint (elastic RTM operator) pass the dot-product test. The conjugate gradient least-squares method is used to solve the least-squares migration quadratic optimization problem. In other words, the Hessian operator for elastic LSRTM is implicitly inverted via a matrix-free algorithm that only requires the action of forward and adjoint operators on vectors. The diagonal of the pseudo-Hessian operator is used t...

3D vector finite-element electromagnetic forward modeling for large loop sources using a total-field algorithm and unstructured tetrahedral grids

Abstract: Unstructured tetrahedral grids with local refinement facilitate the use of total-field solution approaches to geophysical electromagnetic (EM) forward problems. These approaches, when combined with the vector finite-element (FE) method and with refinement near transmitters and receivers, can give accurate solutions and can easily handle realistic models with complex geometry and topography. We have applied this approach to 3D forward modeling for fixed- and moving-loop configurations. MUMPS, a direct solver, was used to solve the linear system of equations generated by FE analysis. A direct solver is particularly suited to the moving-loop configuration for which the right side is different for every transmitter loop, but for which the coefficient matrix is unchanged. Therefore, the coefficient matrix need only be factorized once, and then the system can be solved efficiently for all different right sides. We compared our results with several typical scenarios from the literature: a conductive bric...

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Abstract: We have developed a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geologic data. In this framework, probabilistic geologic modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then uses petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-squares framework to capture and integrate information related to geophysical, petrophysical, and geologic data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geologic field data, we have generated a set of geologic models, which we used to derive a probabilistic geologic model. In this synthetic case study, we found that the integration of geologic information and petrophysical con...