Seismic Assisted History Matching Using Binary Image Matching

Abstract

This paper presents a history matching scheme that has been applied to production data and time lapse seismic data. The production data objective function is calculated using the conventional least squares method between the historical production data and simulation predictions, while the seismic objective function uses the Hamming distance between two binary images of the gas distribution (presence of gas (1) or absence of gas (0)) sequenced over the different acquisition times. The technique is applied to a UKCS (United Kingdom Continental Shelf) field that has deep-water tertiary turbidite sands and multiple stacked reservoirs defining some degree of compartmentalisation. Thirty five parameters are perturbed in this history match, they can be classified as volumetric parameters (net-to-gross, pore volume), transmissibility parameters (permeability, transmissibility), and end points of the relative permeability curves (critical saturation points). An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process. It is found that permeability and critical gas saturation are key parameters for achieving a good history match, and that the volumetric parameters are not significant for this match in this particular reservoir. We also observe that matching only to production data marginally improves the seismic match, whilst matching to only seismic data improves the fit to production data. Combining both sets of data delivers an improvement for the production data and seismic data, as well as an overall reduction in the uncertainties. A unique feature of this technique is the use of the Hamming distance metric for seismic data history matching analysis, as this circumvents the use of the uncertain petroelastic model. This approach is easy to implement, and also helps achieve an effective global history match.

Scale effects on modelling the seismic signature of gas: results from an outcrop analogue

“…We know from laboratory experiments that free gas is known to have a substantial effect on seismic wave properties (Han and Batzle ,b), however, the impact of the gaseous phase could potentially be counteracted by the oil now becoming less ‘lively’ due to the loss of the lighter gas components. To understand whether this contribution is significant, Obidegwu () modelled the fluid properties of the oil–gas mixture by taking into account the effect of the released gas in API and the gas–oil ratio (Rs) and compared them to the fluid properties of the oil–gas mixture ignoring these oil changes, and found that fluid substitution calculations and resultant interpretations can ignore the oil phase changes to first order. Hence, in our calculations of fluid substitution, the oil properties remain constant.…”

“…(b) Pressure depletion condition in the oil leg, with the field pressure dropping below the bubble point. As pressure decreases below the bubble point, the gas saturation builds progressively as gas bubbles are first nucleated, and then coalesce or grow more by the diffusion of additional free gas (Falahat et al 2014;Obidegwu 2015). When a significant number of bubbles are liberated, the fluid system reaches the critical gas saturation, for which the gas becomes mobile.…”

Feature-Oriented Joint Time-Lapse Seismic and Electromagnetic History Matching Using Ensemble Methods

“…Even though there are a number of general mitigation techniques (e.g., localization [7] and inflation [30]) available, each of them has its own drawbacks limiting its efficiency in real applications [29]. Some recent research has been focused on seeking a sparse representation efficiently reducing the number of original data while still capturing the essential information contained in the data [54,40,33,57].…”

Fast selection of geologic models honoring CO2 plume monitoring data using Hausdorff distance and scaled connectivity analysis

“…Zhu et al (2015) compared the area of the CO 2 plumes instead of the quantitative CO 2 saturations for the Sleipner benchmark model because the simulated CO 2 saturations are not fairly matched to the observed CO 2 saturations. Obidegwu et al (2015) measured the dissimilarity between binary images of gas distributions obtained from seismic data using the Hamming distance (Hamming, 1950).…”