Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Reexamination Certificate
2000-02-18
2002-04-16
McElheny, Jr., Donald E. (Department: 2862)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
C367S073000, C702S014000
Reexamination Certificate
active
06374185
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention is related to the use of well data and seismic data to predict subsurface lithology.
2. Description of Related Art
For many years seismic exploration for oil and gas has been conducted by use of a source of seismic energy and the reception of the energy generated by the source by an array of seismic detectors. On land, the source of seismic energy may be a high explosive charge or another energy source having the capacity to deliver a series of impacts or mechanical vibrations to the earth's surface. Acoustic waves generated by these sources travel downwardly into the earth's subsurface and are reflected back from strata boundaries and reach the surface of the earth at varying intervals of time, depending on the distance traveled and the characteristics of the subsurface traversed. These returning waves are detected by the sensors, which function to transduce such acoustic waves into representative electrical signals. The detected signals are recorded for later processing using digital computers. Typically, an array of sensors is laid out along a line to form a series of detection locations. More recently, seismic surveys are conducted with sensors and sources laid out in generally rectangular grids covering an area of interest, rather than along a single line, to enable construction of three dimensional views of reflector positions over wide areas. Normally, signals from sensors located at varying distances from the source are added together during processing to produce “stacked” seismic traces. In marine seismic surveys, the source of seismic energy is typically air guns. Marine seismic surveys typically employ a plurality of sources and/or a plurality of streamer cables, in which seismic sensors are mounted, to gather three dimensional data.
Initially, seismic traces were used simply for ascertaining formation structure. However, in 1979, Taner et al. published the work “Complex Seismic Trace Analysis”, Geophysics, Volume 44, pp. 1041-1063, and exploration geophysicists have subsequently developed a plurality of time-series transformations of seismic traces to obtain a variety of characteristics that describe the traces, which are generally referred to as “attributes”. Attributes may be computed prestack or poststack. Poststack attributes include reflection intensity, instantaneous frequency, reflection heterogeneity, acoustic impedance, velocity, dip, depth and azimuth. Prestack attributes include moveout parameters such as amplitude-versus-offset (AVO), and interval and average velocities.
It has been observed that specific seismic attributes are related to specific subsurface properties. For example, acoustic impedance may be related to porosity. Other subsurface properties appear to be related to other seismic attributes, but it may be unclear what the relationship is, as local factors may affect the data in unexpected ways.
It is well known to employ well logs, such as wireline well logs, and data from core samples extracted from wellbores, to accurately determine petrophysical properties of subterranean formations penetrated by the wellbores. Petrophysical properties of subterranean formations which can be obtained from well logging or core sample operations include lithological composition, porosity, and water or hydrocarbon saturation. This information is valuable for determining the presence and extent of hydrocarbons in the area of interest. However, the portion of subsurface formations which can be measured by such well log and core data is limited in areal extent, e.g. to about six to twelve inches around the borehole from which the measurements were taken, and the petrophysical properties of a subterranean formation can vary widely in the interwell locations.
Synthetic seismic traces may be generated from well log data, typically from sonic and formation density logs. As used herein a synthetic seismic trace is an artificial seismic signal developed mathematically from a model of subsurface strata and an assumed signal source. A synthetic seismic trace is useful for demonstrating the form that a real seismic trace should take in response to the geologic conditions near the well.
Frequently, both well logging data and seismic data are available for a region of the earth which includes a subsurface region of interest. Core data may also be available. Typically, the well log data and, if available, the core data, are utilized for constructing a detailed log, or column, of subsurface properties. The seismic data, which includes data gathered in the interwell region of interest, is then utilized to estimate the structure of the subsurface formation extending between well locations. Subsurface formation property mapping, however, is typically based solely on the wireline log and core sample data. More recently, however, a number of proposals have been made for using seismic data gathered from the interwell region to improve the estimation of formation properties in the interwell region.
U.S. Pat. No. 5,444,619, which issued on Aug. 22, 1995 to Hoskins et al. discloses a method for predicting oil reservoir properties which utilizes seismic data and wellbore data. Seismic data are related to wellbore data to determine the approximate intersections of the seismic and wellbore data (i.e. seismic reflectors are correlated to geological markers in the wellbore). Nonrandom matches between the seismic data and the wellbore data were estimated and the relationship between the seismic data and the wellbore data were then calibrated by training an Artificial Neural Network (ANN). The ANN was then used to predict reservoir properties based on the seismic data. The method includes five principal steps. In the first step, seismic data, which are measured in time, and well data, which are measured in depth, are correlated to relate seismic reflectors to geological markers in the well data. In the second step, wellbore data from multiple wells are extracted from horizon intersections and a significance estimation is utilized to calculate the probability of specific seismic attributes and specific wellbore data not being randomly related. In the third step, a linear calibration is performed between reservoir properties and seismic data, for relationships that are linear. For nonlinear relationships an artificial neural network (ANN) is utilized to learn a nonlinear model using example well data and seismic data. In the fourth step, the reservoir property at locations of interest between wells is calculated by inputting seismic attributes at the locations of interest to the trained ANN, which calculates the reservoir property. In the fifth step, for estimates which do not exactly agree with borehole measurements, geostatistical methods, such as cokriging or gridding the differences between the calibrated attributes, are used to produce a seismic guided estimate that complies with the well data.
U.S. Pat. No. 5,691,958, which issued on Nov. 25, 1997 to Calvert et al. discloses a method for predicting properties of a subsurface formation which utilizes data from a calibration well and a set of seismic traces from the subsurface formation. A synthetic seismogram is generated which is representative of the subsurface formations proximate to the calibration well. A study interval of the subsurface formation is identified and this interval is identified on both the synthetic seismogram and the seismic data traces. One or more seismic attributes are selected for calibration and the calibration attributes are extracted from the study interval on both the synthetic seismogram and the seismic data traces. Subsurface formation properties proximate to the calibration well are determined from the well data. One or more seismic data traces which have calibration attributes for the study interval which approximate those of the synthetic seismogram are selected. A calibration model is then constructed for the subsurface formation using the calibration attributes extracted firm the selected seismic data traces and the form
Derzhi Naum M.
Taner M. Turhan
Walls Joel D.
McElheny Jr. Donald E.
RDSP I L.P.
Thigpen E. Eugene
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