Statistical discrimination of lithofacies from pre‐stack seismic data constrained by well log rock physics: Application to a North Sea turbidite system

1998 ◽  
Author(s):  
Per Avseth ◽  
Tapan Mukerji ◽  
Gary Mavko ◽  
Tor Veggeland
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Rock Physics ◽  
Statistical Discrimination ◽  
Well Log ◽  
Turbidite System

Seismic reservoir mapping from 3‐D AVO in a North Sea turbidite system

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 1157-1176 ◽  
Author(s):  
P. Avseth ◽  
T. Mukerji ◽  
A. Jørstad ◽  
G. Mavko ◽  
T. Veggeland
Keyword(s):  
Monte Carlo ◽  
North Sea ◽  
Seismic Data ◽  
Rock Physics ◽  
Well Log ◽  
Pore Fluids ◽  
Seismic Properties ◽  
Feeder Channel ◽  
Turbidite System ◽  
Seismic Amplitudes

We present a methodology for estimating uncertainties and mapping probabilities of occurrence of different lithofacies and pore fluids from seismic amplitudes, and apply it to a North Sea turbidite system. The methodology combines well log facies analysis, statistical rock physics, and prestack seismic inversion. The probability maps can be used as input data in exploration risk assessment and as constraints in reservoir modeling and performance forecasting. First, we define seismic‐scale sedimentary units which we refer to as seismic lithofacies. These facies represent populations of data (clusters) that have characteristic geologic and seismic properties. In the North Sea field presented in this paper, we find that unconsolidated thick‐bedded clean sands with water, plane laminated thick‐bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the [Formula: see text] ratio helps resolve these ambiguities. We establish a statistically representative training database by identifying seismic lithofacies from thin sections, cores, and well log data for a type well. This procedure is guided by diagnostic rock physics modeling. Based on the training data, we perform multivariate classification of data from other wells in the area. From the classification results, we can create cumulative distribution functions of seismic properties for each facies. Pore fluid variations are accounted for by applying the Biot‐Gassmann theory. Next, we conduct amplitude‐variation‐with‐offset (AVO) analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO responses related to the inherent natural variability of each seismic lithofacies using a Monte Carlo technique. Based on the Monte Carlo simulation, we generate bivariate probability density functions (pdfs) of zero‐offset reflectivity [R(0)] versus AVO gradient (G) for different facies combinations. By combining R(0) and G values estimated from 2‐D and 3‐D seismic data with the bivariate pdfs estimated from well logs, we use both discriminant analysis and Bayesian classification to predict lithofacies and pore fluids from seismic amplitudes. The final results are spatial maps of the most likely facies and pore fluids, and their occurrence probabilities. These maps show that the studied turbidite system is a point‐sourced submarine fan in which thick‐bedded clean sands are present in the feeder‐channel and in the lobe channels, interbedded sands and shales in marginal areas of the system, and shales outside the margins of the turbidite fan. Oil is most likely present in the central lobe channel and in parts of the feeder channel.

Mapping lithofacies and pore‐fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 988-1001 ◽  
Author(s):  
T. Mukerji ◽  
A. Jørstad ◽  
P. Avseth ◽  
G. Mavko ◽  
J. R. Granli
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
Rock Physics ◽  
Normal Incidence ◽  
Seismic Inversion ◽  
Training Data ◽  
Well Log ◽  
Statistical Rock Physics

Reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization. In this study, we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids. One of the innovations was to use a seismic impedance attribute (related to the [Formula: see text] ratio) that incorporates far‐offset data, but at the same time can be practically obtained using normal incidence inversion algorithms. The methods were applied to a North Sea turbidite system. We incorporated well log measurements with calibration from core data to estimate the near‐offset and far‐offset reflectivity and impedance attributes. Multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations. A training data was set up using Monte Carlo simulations based on the well log—derived probability distributions. Fluid substitution by Gassmann’s equation was used to extend the training data, thus accounting for pore fluid conditions not encountered in the well. Seismic inversion of near‐offset and far‐offset stacks gave us two 3‐D cubes of impedance attributes in the interwell region. The near‐offset stack approximates a zero‐offset section, giving an estimate of the normal incidence acoustic impedance. The far offset stack gives an estimate of a [Formula: see text]‐related elastic impedance attribute that is equivalent to the acoustic impedance for non‐normal incidence. These impedance attributes obtained from seismic inversion were then used with the training probability distribution functions to predict the probability of occurrence of the different lithofacies in the interwell region. Statistical classification techniques, as well as geostatistical indicator simulations were applied on the 3‐D seismic data cube. A Markov‐Bayes technique was used to update the probabilities obtained from the seismic data by taking into account the spatial correlation as estimated from the facies indicator variograms. The final results are spatial 3‐D maps of not only the most likely facies and pore fluids, but also their occurrence probabilities. A key ingredient in this study was the exploitation of physically based seismic‐to‐reservoir property transforms optimally combined with statistical techniques.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Seismic reservoir characterization of Utica-Point Pleasant shale with efforts at fracability evaluation — Part 2: A case study

2018 ◽  
Vol 6 (2) ◽  
pp. T325-T336 ◽  
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
James Keay ◽  
Hossein Nemati ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
X Ray Diffraction ◽  
Impedance Inversion ◽  
Seismic Reservoir ◽  
Petrophysical Modeling

The Utica Formation in eastern Ohio possesses all the prerequisites for being a successful unconventional play. Attempts at seismic reservoir characterization of the Utica Formation have been discussed in part 1, in which, after providing the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, and building of robust low-frequency models for prestack simultaneous impedance inversion were explained. All these efforts were aimed at identification of sweet spots in the Utica Formation in terms of organic richness as well as brittleness. We elaborate on some aspects of that exercise, such as the challenges we faced in the determination of the total organic carbon (TOC) volume and computation of brittleness indices based on mineralogical and geomechanical considerations. The prediction of TOC in the Utica play using a methodology, in which limited seismic as well as well-log data are available, is demonstrated first. Thereafter, knowing the nonexistence of the universally accepted indicator of brittleness, mechanical along with mineralogical attempts to extract the brittleness information for the Utica play are discussed. Although an attempt is made to determine brittleness from mechanical rock-physics parameters (Young’s modulus and Poisson’s ratio) derived from seismic data, the available X-ray diffraction data and regional petrophysical modeling make it possible to determine the brittleness index based on mineralogical data and thereafter be derived from seismic data.

scholarly journals Identification of hydrocarbons in chalk reservoirs from surface seismic data: South Arne field, North Sea

2005 ◽  
Vol 7 ◽  
pp. 13-16
Author(s):  
Peter Japsen ◽  
Anders Bruun ◽  
Ida L. Fabricius ◽  
Gary Mavko
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Seismic Data ◽  
Pore Space ◽  
Sedimentary Rocks ◽  
Rock Physics ◽  
Acoustic Properties ◽  
P Wave ◽  
S Wave ◽  
Reservoir Rocks

Seismic data are mainly used to map out structures in the subsurface, but are also increasingly used to detect differences in porosity and in the fluids that occupy the pore space in sedimentary rocks. Hydrocarbons are generally lighter than brine, and the bulk density and sonic velocity (speed of pressure waves or P-wave velocity) of hydrocarbon-bearing sedimentary rocks are therefore reduced compared to non-reservoir rocks. However, sound is transmitted in different wave forms through the rock, and the shear velocity (speed of shear waves or S-wave velocity) is hardly affected by the density of the pore fluid. In order to detect the presence of hydrocarbons from seismic data, it is thus necessary to investigate how porosity and pore fluids affect the acoustic properties of a sedimentary rock. Much previous research has focused on describing such effects in sandstone (see Mavko et al. 1998), and only in recent years have corresponding studies on the rock physics of chalk appeared (e.g. Walls et al. 1998; Røgen 2002; Fabricius 2003; Gommesen 2003; Japsen et al. 2004). In the North Sea, chalk of the Danian Ekofisk Formation and the Maastrichtian Tor Formation are important reservoir rocks. More information could no doubt be extracted from seismic data if the fundamental physical properties of chalk were better understood. The presence of gas in chalk is known to cause a phase reversal in the seismic signal (Megson 1992), but the presence of oil in chalk has only recently been demonstrated to have an effect on surface seismic data (Japsen et al. 2004). The need for a better link between chalk reservoir parameters and geophysical observations has, however, strongly increased since the discovery of the Halfdan field proved major reserves outside four-way dip closures (Jacobsen et al. 1999; Vejbæk & Kristensen 2000).

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