Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Abstract

We introduce a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geological data. In this framework, probabilistic geological modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then utilizes petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-square framework to capture and integrate information related to geophysical, petrophysical and geological data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geological field data we have generated a set of geological models, which we used to derive a probabilistic geological model. In this synthetic case study, we show that the integration of geological information and petrophysical constraints in geophysical joint inversion can reduce uncertainty and improve imaging. In particular, the use of petrophysical constraints retrieves sharper boundaries and better reproduces the statistics of the observed petrophysical measurements. The integration of probabilistic geological modeling permits more accurate retrieval of model geometry, and better constrains the solution 2 while still satisfying the statistics derived from geological data. The analysis of statistical indicators at each step of the workflow shows that 1) the inversion methodology is effective when applied to complex geology, and 2) the integration of prior information and constraints from geology and petrophysics significantly improves the inversion results while decreasing uncertainty. Lastly, the analysis of uncertainty to the integration of the conditioned petrophysical constraints also shows that, for this example, the best results are obtained for joint inversion using petrophysical constraints spatially conditioned by geological modeling. * i-th geological model cell Conditioning of petrophysical constraints

Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization

“…This section introduces the proof of concept of the proposed method through an idealized case study illustrating the potential of the proposed inverse modelling scheme to ameliorate inversion results and to reduce interpretation uncertainty. We use the same 3-D density contrast model as Giraud et al (2017), which is obtained by populating the structural framework of Pakyuz-Charrier et al (2018b). We simulate a series of PGMs sought to represent expected values as well as possible extreme scenarios.…”

“…Likewise, geological modelling and geophysical inversions are routinely performed in the same area to obtain a subsurface model consistent with geological and geophysical measurements (Guillen et al, 2008;Lelièvre and Farquharson, 2016;Pears et al, 2017;Williams, 2008). When sufficient prior information is available, petrophysical constraints can be used in inversion (Lelièvre et al, 2012;Paasche and Tronicke, 2007) and integrated with geological modelling to derive local constraints (Giraud et al, 2017). However, in exploration scenarios, this can be impractical as the available petrophysical information may be insufficient to allow us to derive such constraints (Dentith and Mudge, 2014).…”

“…We introduce a new technique that integrates local uncertainty information derived from probabilistic geological modelling in the inversion of potential field data, following recommendations of Jessell et al (2010Jessell et al ( , 2014Jessell et al ( , 2018, Lindsay et al (2013aLindsay et al ( , 2014 and Wellmann et al (2014Wellmann et al ( , 2017. In contrast to Giraud et al (2016Giraud et al ( , 2017, who derive local petrophysical constraints from petrophysical measurements and geological modelling results, constraints used in uncertainty-guided inversion are based solely on the local conditioning of a gradient regularization function, thereby offering the possibility to integrate probabilistic geological modelling into geophysical inversion in the absence of sufficient petrophysical information. Such conditioning relies on the calculation of local weights derived from prior geological information.…”

Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging

“…More detailed information about the usage of MCUE results in geophysical inversion can be found in Giraud et al (2017Giraud et al ( , 2019a.…”

“…Numerous authors have since tackled the issue of integrating petrophysical and geological information to model geophysical quantities (seismic velocities, mass density, etc.) through inversion, with an increasing trend in the past 15 years or so (see, for instance, references reviewed in Lelièvre and Farquharson 2016;Meju and Gallardo 2016;Moorkamp et al, 2016;Giraud et al, 2017). In contrast, the recovery of geological quantities from geophysical inversion has seen much less effort.…”