We LHR3 06 Detecting Production Effects and By-passed Pay from 3D Seismic Data Using a Facies Based Bayesian Seismic Inversion

Transcription

1 We LHR3 06 Detecting Production Effects and By-passed Pay from 3D Seismic Data Using a Facies Based Bayesian Seismic Inversion K.D. Waters* (Ikon Science Ltd), A.V. Somoza (Ikon Science Ltd), G. Byerley (Apache Corp) & P. Rose (Apache Corp) SUMMARY We apply new facies based Bayesian simultaneous inversion technology to two 3D seismic datasets covering the Forties field in the UKCS. The inversion jointly solves for seismic facies and their corresponding impedances, without the pre-requisite low frequency background model that other inversion schemes require. The 3D simultaneous AVA inversion was applied separately to two vintages of 3D seismic, acquired in 1988 and The objective of the inversions was to characterise the seismic response into facies (overburden shale, Forties shale, Forties brine and Forties oil reservoir) and impedances and subsequently use these volumes to identify areas of high risk of pre-1988 sweep and remaining attic oil at The inversion of both the 1988 and 2013 data sets was considered excellent, the sand vs shale facies distribution defining the complex reservoir architecture of the Forties Field as proved by some 350 well penetrations. The new inversion technology has resulted in valuable new insights to distribution of remaining hydrocarbons. In this case study it has provided another evaluation tool to be integrated into the Forties bypassed pay target methodology along with other datasets: 4D seismic, existing inversion results, production history and well log data integrated into detailed structural and stratigraphic analysis.

2 Introduction We apply a new facies based Bayesian simultaneous inversion technology to two 3D seismic datasets covering the Forties field in the UK North Sea. The inversion scheme jointly solves for seismic facies and their corresponding impedances, without the pre-requisite low frequency background model that other conventional inversion schemes require. The 3D simultaneous AVA inversion was applied separately to two vintages of 3D seismic, acquired in 1988 and 2013 respectively. The objective of the inversions was to characterise the seismic response into facies (overburden shale, Forties shale, Forties brine reservoir and Forties oil reservoir) and impedances and to subsequently use these volumes to identify areas of high risk of pre-1988 sweep and remaining attic oil at The inversion of both the 1988 and 2013 data sets was considered excellent, the sand vs shale facies distribution defining the complex reservoir architecture of the Forties Field which is proved by some 350 well penetrations. The 1988 survey was shot at a point where 1.8 billion barrels of oil had already been produced from the Forties Field since first production in The 1988 inversion oil sand facies distribution results showed a generally very good calibration to the distribution of remaining hydrocarbon as defined by: water cut evolution in the original development wells, corridors of sweep which can be traced back to high volume injection wells, parts of the reservoir with hydrocarbon sweep responses on D seismic, mid 1990s infill wells, and calibrated to the near virgin state of the Echo platform area in SE Forties (first production 1987). As expected, the 2013 oil sand facies distribution was much more limited, total production by this stage being 2.7 billion barrels. The results calibrated well to infill drilling results obtained post the 2013 seismic survey, predicting both swept and oil bearing locations. However, it was noticed that the oil sand prediction was less successful in some of the stratigraphically deeper parts of the Forties reservoir and in the Charlie channel complex in the west of the field. The new facies based Bayesian simultaneous inversion technology has resulted in valuable new insights to distribution of remaining hydrocarbons in the Forties Field. In this case study it has provided another evaluation tool to be integrated into the Forties bypassed pay target methodology along with the existing datasets: 4D seismic datasets, existing inversion results, production history and well log data integrated into detailed structural and stratigraphic analysis. Theory of the Facies Based Bayesian Simultaneous Inversion Model based seismic inversion is a well-established technique which is widely used in industry. The inversion works by iteratively updating a starting low frequency model by minimising the misfit between synthetic seismic generated from the model and the observed seismic traces, figure 1a. Once the misfit has reached a satisfactory level, the final updated broadband impedance model is output. In the presence of seismic noise, this process can be quite unstable, giving large deviations from the starting model and resulting in unphysical impedance estimates. Therefore, a model weight is typically applied, which penalizes large deviations from the starting model and forces more plausible final impedance estimates, equation (1). (a) (b) Figure 1 Schematic diagram of conventional model based inversion (a, left) and the facies based Bayesian simultaneous scheme (b, right). Only the AI component is shown for simplicity.

3 The model based inversion scheme is described in equation (1) below. 2 (1) Q seismicmisfit modelmisfit WhereQ, is the final broadband impedance output and, is the model weight. The same model based inversion scheme can also be cast as a Bayesian problem (presented in terms of a post stack acoustic inversion for simplicity), as below in equation (2) where the posterior probability, q, of the acoustic impedance given the seismic, is proportional to the likelihood function, L, of the seismic (or in this case forward modelled synthetic) given an acoustic impedance, multiplied by the probability distribution function, p, of the acoustic impedance prior model. (2) qai seismic Lseismic AI pai The new facies based Bayesian simultaneous inversion scheme is presented as a schematic diagram in figure 1b. Unlike the conventional model based inversion, there is no requirement for a single multifacies low frequency model. Rather, depth dependent trends are established for each of the expected facies - typically reservoir and non-reservoir. Given the prior facies based trends, the seismic data and wavelets and a set of prior facies proportions, the inversion solves for both the most likely facies model and its corresponding set of impedances. Equation (2) is thus extended to contain an additional facies term as described in equation (3) below (adapted from Kemper and Gunning, 2014). (3) AI, facies seismic Lseismic AI pai facies pfacies Figure 2 shows a simple slab model constructed using depth trends and rock property averages from the North Sea Palaeocene Forties formation. 5 synthetic angle stacks were created from 5 to Figure 2 Slab model with fluid contacts, constructed using depth trends and average properties a) acoustic impedance b) 5 degree near stack synthetic and c) 45 degree far stack (blue = hard) The synthetic data were then inverted using a conventional model based inversion scheme and the facies based Bayesian simultaneous inversion technique, Figure 3. In order to compare final facies estimates, a second step of Bayesian classification was also applied to the conventional model based inversion results, using the same depth trends. For both inversion schemes, the recovered Facies image is exact and correct when compared to the input model. However, in order to achieve this result using the conventional model based inversion scheme we required knowledge of the reservoir geometry and the presence and location of the saturating fluid types. The facies based Bayesian simultaneous inversion we present in this paper requires no detailed model building but delivers absolute impedances and facies estimates directly. This ensures total consistency of the rock physics, impedance cubes and facies volumes without the pre-requisite and often heavily biased background model.

4 Figure 3 Top row a) shows one of the four low frequency models input into the facies based Bayesian inversion process followed by the inverted acoustic impedance b) and corresponding facies model c). Bottom row - a ) shows the single low frequency model required to correctly invert, via simultaneous inversion to acoustic impedance b ) in order to then derive, via Bayesian classification, c ). Application of the facies based Bayesian Simultaneous Inversion Algorithm to the Forties Field, UKCS Ten key exploration and appraisal wells were selected for the study and used to define the compaction trends and rock physics models. The wells were drilled over a period of time spanning the production period and seismic vintages that were used. The remaining several hundred production and injection wells were used in the quality control of inversion results over the full field area and with knowledge of their age and production profiles relative to the seismic vintages. Two seismic datasets were used during the analysis. The 1988 and 2013 seismic data sets consisted of five angle stacks, with central angles of 8, 16, 24, 32 and 40 degrees. Structural / stratigraphic horizons were available for the Sele Fm, Forties Fm and Maureen Fm. Wavelets were estimated for each of the angle stacks and for each vintage, giving a total of ten wavelets which were used in the study. Figure 4 Stratal slice through 1988 Facies cube, 50ms below top Sele Fm. The facies inversion results were extracted from the 1988 inversion results, figure 4, and compared to active production (green) and injection (blue) wells at the time of and preceding seismic acquisition. In general an excellent correlation is observed between the mature production areas to the North and far South West and the initial production around the clustered production wells in the Echo area in the

5 30 May 2 June 2016 Reed Messe Wien South East. The facies inversion does a good job of identifying the sweep pattern and flood front, with brine bearing predictions forming halos around many of the more mature injector/producer locations, however there are areas where the inversion notably over-predicts the oil distribution, in particular around the Charlie Channel system and in the deeper Upper Main Sand. The facies predictions are also broadly commensurate with sweep responses identified from 4D seismic analysis. Figure 5 Facies extraction in top 20m of major sand bodies based on 2013 inversion results. Figure 5 shows an extraction from the 2013 facies cube for the upper 20m of the Forties reservoir zone. The definition of the OWC is much improved over the 1988 seismic data set with less oil being predicted beyond the field limits. A clear correlation between the positions of the production wells (red squares) and swept areas (brine sand prediction above the OOWC) can be observed, though the inversion still over-predicts the pay distribution in the Charlie channel axis. The results were compared to over 20 infill wells which post-dated the seismic acquisition with the majority showing excellent agreement. Conclusions We have shown proof of concept and initial results of a new facies based Bayesian simultaneous inversion which jointly solves for impedances and facies. It does not require the construction of a conventional low frequency model and therefore reduces bias in the output impedance cubes. It also allows more comprehensive scenario testing to assess the sensitivity of the inversion to e.g. Net:Gross and hydrocarbon prior proportions. The results show excellent calibration to the available well data, which are considerable in this actively producing field. Acknowledgements We would like to acknowledge Apache UK for permission to show these results and CSIRO and the various staff at Ikon Science Ltd in their generation. References Kemper, M.A.C., and Gunning, J. [2014] Joint Impedance and Facies Inversion Seismic Inversion Redefined. First Break, 32(9), th EAGE Conference & Exhibition 2016