AU2013100760A4 - A workflow for seismic lithologic characterization - Google Patents

Abstract

A method is described for evaluating reservoir properties from received seismic data and well log data. A quality of the seismic data is estimated (100) and at least one seismic wavelet is extracted from the seismic data (200). The seismic data is inverted using the at least one seismic wavelet to create an acoustic impedance model with an intercept part and a gradient part (300). At least one value of a rotation for an extended elastic impedance is determined (400) based on the intercept part and the gradient part that allows differentiation between reservoir rocks and other lithologies, for example soft volcanic rocks. Reservoir properties are determined (500, 600) based on the quality of the seismic data, the horizons and the extended elastic impedance. C- 0 C wi "22 L 4$ ~0 U) W LL -a 0 0- a)nAc Ci Q) - U 4) to jQ~C 0w ( to =t$ ~0 > +4 wLC

Description

1 A Workflow For Seismic Lithologic Characterization Field of the invention The present invention relates generally to the processing of geological data and more particularly to a computer-implemented method for evaluating reservoir properties from seismic 5 data. Background of the invention There are many measurement techniques used to evaluate subsurface formations. For example, the geology around a borehole may be evaluated using borehole logging tools. Various properties of the formations through which the borehole penetrates can be measured and plotted 10 with reference to the location of the measurements. Examples of measurements techniques include resistivity/conductivity measurements, acoustics, ultrasound and gamma radiation. Borehole data may be analysed by human interpreters such as petrophysicists to characterize the commercial potential of a well. An alternative or additional approach is to obtain core measurements, in which rock samples are retrieved from the subsurface for analysis in the 15 laboratory. Subsurface formations may also be assessed using seismic data. Seismic excitation is applied by one or more sources and the response measured at a range of receivers. The amplitude versus offset (AVO) of the seismic signal is analysed to infer attributes of the formations. The seismic data may be inverted to evaluate reservoir properties. 20 Drilling wells, for example to extract hydrocarbons, is a complex and expensive operation. Given the cost of drilling and the production delays that may arise from inappropriate drilling, there is a strong incentive to characterize subsurface formations as accurately as possible from the data available. Net hydrocarbon pay estimation based on seismic data requires identification of likely 25 reservoir sands. This identification can be difficult in complex lithologies like volcanics. In the weathered soft form, volcanics display similar acoustic impedance properties to reservoir sands.

2 It is also desirable to streamline the approaches taken by petrophysicists and geophysicists, avoiding duplication of effort and providing consistency. Reference to any prior art in the specification is not, and should not be taken as, an acknowledgment or any form of suggestion that this prior art forms part of the common general 5 knowledge in Australia or any other jurisdiction or that this prior art could reasonably be expected to be ascertained, understood and regarded as relevant by a person skilled in the art. Summary of the invention According to a first aspect of the invention there is provided a method for evaluating reservoir properties from seismic data, the method comprising: 10 a. receiving the seismic data and well log data; b. estimating a quality of the seismic data; c. extracting at least one seismic wavelet from the seismic data; d. inverting the seismic data using the at least one seismic wavelet to create an acoustic impedance model with an intercept part and a gradient part; 15 e. determining at least one value of a rotation for an extended elastic impedance based on the intercept part and the gradient part that allows differentiation between reservoir rocks and other lithologies, for example soft volcanic rocks; f interpreting horizons on the acoustic impedance model; and g. evaluating reservoir properties based on the quality of the seismic data, the 20 horizons and the extended elastic impedance. An intial feasibility study may evaluate the received well log data using an extended elastic impedance crossplot to assess differentiation between the reservoir rocks and the soft volcanic rocks. The determining step may use crossplots of the intercept and gradient to determine the at 25 least one rotation. Well log data may also be used to determine the at least one rotation.

3 The method may include static modelling of at least one of net-to-gross reservoir rocks and porosity. Well placement may be determined based on the evaluated reservoir properties. According to a second aspect of the invention there is provided a system for evaluating reservoir properties from seismic data, the system comprising: 5 h. a program storage device; i. a dataset storage device; j. a display device; k. a user input device; and 1. a computer system configured to interact with the user input device, the display 10 device, the data storage device and the program storage device to execute the programs to perform a method comprising: i. estimating a quality of the seismic data; ii. extracting at least one seismic wavelet from the seismic data; iii. inverting the seismic data using the at least one seismic wavelet to create 15 an acoustic impedance model with an intercept part and a gradient part; iv. determining at least one value of a rotation for an extended elastic impedance based on the intercept part and the gradient part that allows differentiation between reservoir rocks and other lithologies such as soft volcanic rocks; 20 v. interpreting horizons on the acoustic impedance model; and vi. evaluating reservoir properties based on the quality of the seismic data, the horizons and the extended elastic impedance. As used herein, except where the context requires otherwise, the term "comprise" and variations of the term, such as "comprising", "comprises" and "comprised", are not intended to 25 exclude further additives, components, integers or steps.

4 Further aspects of the present invention and further embodiments of the aspects described in the preceding paragraphs will become apparent from the following description, given by way of example and with reference to the accompanying drawings. Brief description of the drawings 5 Fig. 1 is a schematic flow diagram of a workflow for seismic reservoir characterization. Fig. 2 is an example of a goodness of fit plot of AVO data; Fig 3A is an example of a well-to-seismic tie; Fig. 3B is an example of an extracted wavelet from the data of Fig. 3A; Figs. 4A and 4B show an example of the optimization of a coloured inversion operator; 10 Fig. 4C illustrates the results of the coloured inversion; Fig. 5A is an example of an AIGI crossplot for shales before rotation; Fig. 5B is an example of an AIGI crossplot for volcanics before rotation; Fig. 6 is an example of horizon interpretation using the rotated crossplot information; Fig. 7A is an example of an AIGI crossplot for shales after rotation; 15 Fig. 7B is an example of an AIGI crossplot for volcanics after rotation; Figs. 8A and 8B show an example of the extraction of volcanic geobodies using the workflow of Fig. 1; Fig. 9 is an example of the sesmic net pay estimation used in the EEI attribute extraction process of Fig. 1; 20 Figs. 10A, 1OB and 10C illustrate static modelling resulting from the workflow of Fig. 1 Fig. lb is a schematic illustration of a computer network on which the workflow of Fig. 1 may be implemented.

5 Detailed description of the embodiments Figure 1 illustrates the general workflow for estimating reservoir properties from seismic data. The workflow 1 may be implemented as software running on a computer network such as that illustrated in Fig. 11. Software running on the computer network may guide a user through 5 the different stages of the workflow 1. Individual processes in the workflow may be implemented in dedicated software modules. The workflow serves to guide the user through the various processes in a streamlined fashion, encouraging integration between the formation evaluation (FE) and geophysics disciplines. In an initial stage 100 a feasibility study is conducted. A EEI-AIGI crossplot analysis is 10 conducted to assess the extent to which the available well-log data can extract the desired lithologies, for example soft volcanics. In the feasibility study, a quality control approach is also used to evaluate the available seismic data. The following process 200 in the workflow executes a well-to-seismic tie and wavelet extraction. Then, in process 300 a coloured inversion process is applied to invert the seismic data to acoustic impedance (AI). 15 In process 400, the extended elastic impedance (EEI) of the formation is used to construct cross-plots of acoustic impedance (AI) and gradient impedance (GI). In process 400, the rotation angles of the cross-plots are optimised to determine optimal fluid and ortho projection angles. A process of horizon interpretation 500 follows, in which the AVO attribute volumes are used to interpret the tops and bases of the reservoirs and formations.. This is followed by a 20 process 600 of extracting EEI attributes of the formations. The extracted data attributes are used in a static modelling process 700 that provides a seismically-contrained estimateof the net-to gross (NTG) assessment and the porosity of the formations. Feasibility studies The workflow 1 makes use of the concept of the extended elastic impedance (EEI), which 25 is a transform initially described in Connolly, P. 1999, 'Elastic Impedance', The Leading Edge, 18, no. 4, 438-452. The elastic impedance (EI) described by Connolly provided a generalisation of acoustic impedance for variable incidence angles. This provided a framework to calibrate and invert seismic data for far-offset stacks. The elastic impedance tends to the acoustic impedance as the offset angle tends to 0.

6 Later, as described for example in Whitcombe, D.N., Fletcher, J.G., 2001, 'The AIGI Cross Plot As An Aid To AVO Analysis And Calibration', SEG International Exposition and Annual Meeting, San Antonio, Texas, 9/14 September, 2001, the concepts of EEI and Gradient Impedence (GI) were introduced. EEI is a generalisation of elastic impedance that allows 5 inversions to be carried out on data that is tuned for lithology or fluids. The GI relates to changes in the gradient reflection coefficient B. The EEI is expressed as a function of angle x, as follows: Va p p P (cos, Sin ) Q=-8K sin g R =(cos -4K sin g) a=-V (m/s = RHOBg (cc) 10 EEI tends to Al as X tends to zero and tends to GI as x tends to 90 degrees. Cross-plotting Al versus GI is a powerful screening tool for estimating reservoir properties from seismic (RPF S).

7 Soft volcanics are responsible for false positives in the search for gas-bearing sands from seismic data. Volcanics have a similar acoustic impedance to gas bearing formations seen elsewhere in the field, which render an analysis of intercept and near stack data insufficient to distinguish between volcanics and reservoir sands. 5 In the feasibility study 100 an AIGI crossplot of the well-log data is performed to assess the extent to which the well-log data can distinguish the required lithologies, for example soft volcanics. In addition, the feasibility studies 100 assess the quality of seismic data across the field. Amplitude versus offset (AVO) modelling depends on the reflection of the seismic wave off an 10 interface between two subsurface media. Various forms of equations are available in the literature to describe the amplitudes of reflected and refracted waves at planar interfaces. One form of these equations is provided in Shuey, R.T. (1985), 'A simplification of the Zoeppritz equations', Geophysics, 50:609-614. Once appropriate simplifications have been made (for example by assuming that the angle of incidence is less than 300) the Shuey 2-term 15 approximation of the reflection coefficient becomes: RC(O) = A + B(sin 2 0) where 0 is the angle of incidence; A is the reflection coefficient at normal incidence, which is a function of the contrast in acoustic impedances between the media; 20 B is the gradient of the reflection as a function of the increase in the angle of incidence. In the feasibility studies 100, statistical values are employed to determine the quality of the AVO calculations. In this approach, the differences between the actual amplitudes picked and the expected values based on the Shuey 2-term approximation are assessed. A goodness of fit measure may be obtained using the following equation: 25 R2 =(yi _ 5)2 y(yi'y) 2 where N is the number of picks in one gather and the summations run from i=1 to N; y ' is an actual picked amplitude value; y' is the mean of the actual picked amplitude values; 8 y is an expected amplitude value using the Shuey 2-term approximation; and y is the mean of the expected amplitudes using the Shuey 2-term approximation. Fig. 2 illustrates an actual example of goodness of fit values across a region of operation. Fig. 2 shows a plan view of the region, which includes 4 bore holes, marked X1, X2, X3 and Yl. 5 Across the field the goodness of fit averages between around 0.6 and 0.7. Although the scale is obscured by reproduction of Fig. 2, the goodness of fit was found to be higher towards the north west area of the field due to the reservoir thickness. The reliability of the AVO data tends to decrease towards the south east (i.e. the bottom right corner of Fig. 2) due to reservoir thinning. The GOF data that occurs in process 100 provides a measure of the quality of the seismic 10 data across the field. Areas of low GOF may help explain sub-optimal results. If the GOF is uniformly very low across the entire filed there may be a problem with the seismic data that should be analysed by considering the pre-stack gathers. The feasibility studies may be performed using the GEOLOG software available from Paradigm. 15 Wavelet extraction Process 200 relates to the integration of seismic data with well log data. In one arrangement, process 200 may be executed using the Petrel software package available from Schlumberger Limited. In one approach, the Roy White wavelet estimation algorithms are used for estimating the seismic wavelet by calibrating and correlating well log data and seismic data. 20 In the field, checkshot data may be obtained, for example by lowering a geophone to the formation of interest. Energy may be sent out from the surface and the resultant signal measured by the geophone. The checkshot data may be used to calibrate sonic logs so that they tie with seismic data. The calibrated sonic log may be used to generate synthetic seismic data. Fig. 3A illustrates some experimental results. The figure shows a calibrated sonic log 201 25 and a corresponding AVO stack 202 and the synthetic 202A. From this data, the processing of algorithm determines the best trace location at which to extract the wavelet. Given that tie location, the optimal wavelet is determined. Fig. 3B shows an example in which there is a plot 203 of power spectrum versus frequency and a plot 204 of phase spectrum against frequency together with a plot 205 of the extracted seismic wavelet, plotted against time on the x-axis.

9 The extracted wavelet is used in later stages of the workflow 1 for inverting seismic data to impedance values. Coloured inversion In process 300, the seismic data is inverted to acoustic impedance, which may be done, 5 for example, using a coloured inversion approach. In one arrangement, this process may be executed in the Hampson-Russell software available from CGGVeritas. The process involves a band-limited inversion. The seismic spectrum may be shaped to a power-law earth spectrum. A shift operator is used to provide a phase shift of -90 degrees. The coloured inversion is scaled to match acoustic impedance and elastic impedance (AI/EI) information derived from filtered well 10 data. Figs. 4A-4C illustrate an iterative coloured inversion operator optimization. Fig. 4A shows 4 plots from an application of a coloured inversion. Plot 305 is a log-frequency plot of the acoustic impedance. The x-axis is log frequency and the y-axis is amplitude. The coloured inversion operator is a linear fit 306 through the cloud of impedance data. The amplitude 15 decreases in a linear fashion relative to the log of frequency. In the iterative optimisation the intercept and gradient of operator 306 are varied to obtain a good match between the well and seismically derived acoustic impedance data (eg traces 330 and 331 in Fig. 4B). The analyst using the workflow of Fig. 1 adjusts the parameters of the operator 306 and observes the traces as in Fig. 4B until a suitable match is obtained. 20 Plot 307 shows the frequency spectrum of the seismic data. Plots 309 and 311 are examples of the time response and frequency response, respectively, of the coloured inversion operator used to invert the seismic data in this example. Fig. 4B shows two examples of the inversion analysis of post-stack data, deriving from wells X1 and X2 respectively (having relative positions as shown in Fig. 2 25 In Fig. 4B the vertical axis 335 is time. Using the inversion operator, an inverted trace 330 is compared to the original data 331 from well X1 to evaluate the match. The wavelet extracted in process 200 may be convolved with the acoustic impedance to provide the synthetics data 332, which may be compared to the real seismic data 333. Fig. 4B also includes an example from well X2 10 Fig. 4C illustrates the improved results available using the workflow of Fig. 1. Plot 337 shows an example of intercept (AI) values for the wells X1-X3 and Y1 of Fig. 2 based on reflectivity data (ie not using the analysis of stages 100-300). In Fig. 4C the y-axis represents depth below the surface and the x-axis is horizontal distance along the surface, as illustrated in 5 the insert map 339. Plot 338 is an example of Al values using the inverted data of process 300. The colour (although obscured in reproduction) in plot 338 represents the Al value. Although obscured in black and white reproduction, plot 338 provides clearer insight into the relevant formations than plot 337 based on reflectivity. EEI-AIGI volume rotations 10 Whitcombe and Fletcher state that while x was introduced as a mathematical transform, it can be visualised as a rotation angle on the AIGI crossplot. It permits the analyst to ascertain a co-ordinate rotation to discriminate sands from shales, gas from water, and poor quality from good quality formations. Log data plotted in AIGI space can define fluid or lithological projections to maximise an impedance contrast. 15 In process 400, AIGI volume rotations are performed on the inverted data available as an output of process 300. The AIGI rotations of process 400 may be executed using the Petrel software package available from Schlumberger Limited. An example is shown in Figs. 5 6, and 7using seismic logs generated around the wellbores X1-X3 and Y1 shown in Fig. 2. Fig. 5A is an example of an AIGI crossplot for shales before rotation. The x-axis is Al, the y-axis is GI and the 20 z-values are indicated as colours relating to shale values. Fig. 5B is a counterpart AIGI crossplot where the z-values relate to volcanics. In process 400 the optimal fluid and ortho-fluid projection angles are determined, i.e. an angle x is determined to distinguish volcanics. The technique is described in Whitcombe and Fletcher (2001). In the example of Figs. 5A and 5B the optimal projection is shown as line 401. 25 Fig. 6 shows data from well X-01 generated during determination of the optimal angle of rotation. Curve 410 is a gamma-ray record from the well and curve 411 is acoustic impedance generated from the well. Curve 412 is acoustic impedance generated from the inverted data obtained in process 300.

11 Curve 413 shows seismic log EEI data using a rotation of x = 15 degrees. This was found to be the the most useful rotation in the present application. Curves 414 show seismic log EEI data using a rotation of x = 20 and 25 degrees respectively. The set of curves 417 shows the orthogonal angle rotations corresponding to x = 15, 20 and 25 degrees respectively (ie angles 5 105, 110 and 115 degrees). Curve 418 shows interpreted data from the well log, indicating bands (eg 420) that are regarded as volcanics. Figs. 7A and 7B show examples of the data of Figs. 5A and 5B rotated with x = 15 degrees. In Fig. 7 the x-axis is the EEI at a projection of 15 degrees and the y-axis is the EEI at the ortho 10 fluid projection of (90 + 15)= 105 degrees. The volcanics plot out showing the non-paying lithologies to the right. The paying lithologies plot out to the left of the rotated crossplots, ie at low fluid projection amplitudes. The lithologies with higher shale volume (VSH) plot out to the right of the rotated crossplots. 15 Horizon interpretation The rotation of the crossplots enables the creation of rotated sesmic volumes, optimally rotated to show fluid or lithologies. These rotated volumes may then be used for interpretation of the subsurface horizons in process 500, which may also be executed using the Petrel software package available from Schlumberger Limited. 20 EEI attribute extractions The following stage of the workflow 1 is the process 600 of extracting EEI attributes from the data, which may also be performed in the Petrel software package available from Schlumberger Limited. An example is shown in Figs. 8A and B which illustrate the extraction of volcanic geobodies 25 using a crossplot volume interpretation of the AVO attribute volumes. Fig. 8A shows a crossplot of the rotated volumes, using the rotation of 15 degrees. A system user may highlight regions of the crossplot, for example the polygon 503. As mentioned, this region of the cross plot 12 corresponds to non-paying lithologies. The selected region of the crossplot is then displayed as coloured regions in several views of the data to assist the system user in analysing the geobodies. Examples of such view are shown as plot 505 in Fig. 8A, which shows data in the region of well X-03. 5 Two three-dimensional views generated by the software are shown in Fig. 8B, where arrows indicate regions of volcanic geobodies. In practice these views are displayed in colour, for example on display 90, providing useful information to the system user. The information is obscured to some extent in the black and white images reproduced herein. Seismic Net Pay estimation 10 The EEI attribute extraction may include a seismic net pay estimation. This estimation may for example use the technique described in Connolly, P. "A simple, robust algorithm for seismic net pay estimation", The Leading Edge October 2007 v. 26 no. 10 p. 1278-1282. This calculation is available, for example, in Schlumberger's Petrel software. This is illustrated in Fig. 9, which includes three views of the region with wells X-01 15 X03 and Y-01, ie an EEI threshold view 901, a seismic net pay view 902 and a seismic net-to gross view 903. Net pay estimation uses the generated AVO attribute stacks and the reservoir horizons top/base interpretations (with calibration to available wells) to estimate a seismic net pay. The net pay information may also be used to make well placement decisions and to 20 optimize drilling plans such as horizontal drilling. Static modelling After the EEI has been used to differentiate between the reservoir rocks and other lithologies, such as the volcanic geobodies of Fig. 8, the process of static modelling 700 may be performed. The static modelling may use the seismic data, the acoustic impedance, and/or the 25 rotated seismic volumes from process 400 to calculate net-to-gross reservoir rock volume and/or porosity. Calculations to support this modelling may be done by any standard method, such as those available in Schlumberger's Petrel software. The static modelling presents lithologic 13 information with respect to depth, which is convenient for experts such as geologists reviewing the data. The static modelling is illustrated in Figs. 10A-10C. In Fig. 10A, plot 951 shows a view of the base Q zone of the region including wells X-O1-X-03 and Y-01, based on the EEI data 5 with a rotation of 15 degrees. Although obscured in black and white reproduction, the colour scale 952 indicates the identified lithologies. Plot 951 is thus an output of the workflow of Figure 1. In contrast, plot 950 is a plot of the same region that shows statistically distributed properties without the guidance of seismic properties. It may be seen that the resolution of plot 950 is significantly coarser than the resolution of plot 951 and also shows a different interpretation of 10 the distribution of volcanic geobodies. Thus, drilling a well based on plot 950 rather than plot 951 increases the likelihood of drilling into a non-productive volcanic geobody. The increased resolution afforded by the workflow of Figure 1 may have major cost benefits resulting from improved well placement. Plot 951 uses the EEI data with a rotation of 15 degrees. The rotated data may be filtered, 15 for example as shown in the cross plot 955 which has the fluid projection on the x-axis and the ortho projection on the y axis. Data with a relative EEI less than zero is filtered out. Plot 957 in Fig. 1OC shows a resulting plot of the region using the filtered data. Plot 957 indicates some identified volcanics geobodies. The location of these identified geobodies is different to the location of volcanic geobodies in the plot 950. 20 Plot 959 is an example of EEI data sampled into a static model. Figure 11 schematically illustrates an example of a computer network 84, into which implementations of various technologies described herein may be implemented. The computer network 84 may include a data processing system or computer system 88, which may be implemented as any conventional personal computer or server. However, those skilled in the art 25 will appreciate that implementations of various technologies described herein may be practiced in other computer system configurations, including hypertext transfer protocol (HTTP) servers, hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, Linux computers, mainframe computers, and the like. The computer system 88, comprising at least one processor, may be in communication 30 with at least one disk storage or at least one memory device 86 and 96, which may be external 14 hard disk storage devices. It is contemplated that disk storage devices 86 and 96 are conventional hard disk drives, and as such, will be implemented by way of a local area network or by remote access. Of course, while disk storage devices 86 and 96 are illustrated as separate devices, a single disk storage device may be used to store any and all of the program instructions, 5 measurement data, and results as desired. In one implementation, data sets including seismic and well bore data may be stored as computer storage media in disk storage device 96. The computer system 88 may retrieve the appropriate data from the disk storage device 96 to process the data according to program instructions that correspond to implementations of various technologies described herein. The 10 program instructions may be written in a computer programming language, such as FORTRAN, C, C++, Java and the like. The program instructions may be stored in a computer-readable medium, such as a program disk storage device 86. Such computer-readable media may include computer storage media and communication media. Computer storage media may include volatile and non-volatile, and removable and non-removable media implemented in any method 15 or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data. Computer storage media may further include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory or other solid state memory technology, CD-ROM, digital versatile disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, 20 magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing system 88. Software instructions running on the system computer 88 may be used to implement computational steps in the workflow 1 of Figure 1. The system may include software to guide an analyst through the workflow. Individual modules of the workflow may be executed in different 25 software packages running on the computer network 84, including the Petrel, Hampson-Russell and Geolog packages mentioned above. In one implementation, the computer system 88 may include at least one graphical user interface (GUI) components such as a graphics display 90 and a keyboard 92 which can include a pointing device (e.g., a mouse, trackball, or the like, not shown) to enable interactive operation. 30 The GUI components may be used both to display data and processed data products and to allow the user to select among options for implementing aspects of the method. The computer system 15 88 may store the results of the methods described above on disk storage 86, for later use and further analysis. It will be understood that the invention disclosed and defined in this specification extends to all alternative combinations of two or more of the individual features mentioned or evident 5 from the text or drawings. All of these different combinations constitute various alternative aspects of the invention.

Claims (5)

1. A method for evaluating reservoir properties from seismic data, the method comprising: a. receiving the seismic data and well log data; b. estimating a quality of the seismic data; 5 c. extracting at least one seismic wavelet from the seismic data; d. inverting the seismic data using the at least one seismic wavelet to create an acoustic impedance model with an intercept part and a gradient part; e. determining at least one value of a rotation for an extended elastic impedance based on the intercept part and the gradient part that allows differentiation 10 between reservoir rocks and soft volcanic rocks; f interpreting horizons on the acoustic impedance model; and g. evaluating reservoir properties based on the quality of the seismic data, the horizons and the extended elastic impedance.

2. The method of claim 1 comprising evaluating the received well log data using an 15 extended elastic impedance crossplot to assess differentiation between the reservoir rocks and the soft volcanic rocks.

3. The method of claim 1 or 2 wherein the determining step uses crossplots of the intercept and gradient to determine the at least one rotation and wherein the well log data is also used to determine the at least one rotation. 20

4. The method of any one of the preceding claims wherein the evaluating reservoir properties comprises static modelling of at least one of net-to-gross reservoir rocks and porosity.

5. A system for evaluating reservoir properties from seismic data, the system comprising: a. a program storage device; 25 b. a dataset storage device; 17 c. a display device; d. a user input device; and e. a computer system configured to interact with the user input device, the display device, the data storage device and the program storage device to execute the 5 programs to perform a method comprising: i. estimating a quality of the seismic data; ii. extracting at least one seismic wavelet from the seismic data; iii. inverting the seismic data using the at least one seismic wavelet to create an acoustic impedance model with an intercept part and a gradient part; 10 iv. determining at least one value of a rotation for an extended elastic impedance based on the intercept part and the gradient part that allows differentiation between reservoir rocks and soft volcanic rocks; v. interpreting horizons on the acoustic impedance model; and vi. evaluating reservoir properties based on the quality of the seismic data, the 15 horizons and the extended elastic impedance.