EP0354716A1 - Dispositif et procédé pour éviter le coincement d'un train de tiges en cours de forage - Google Patents

Dispositif et procédé pour éviter le coincement d'un train de tiges en cours de forage Download PDF

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Publication number
EP0354716A1
EP0354716A1 EP89307847A EP89307847A EP0354716A1 EP 0354716 A1 EP0354716 A1 EP 0354716A1 EP 89307847 A EP89307847 A EP 89307847A EP 89307847 A EP89307847 A EP 89307847A EP 0354716 A1 EP0354716 A1 EP 0354716A1
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European Patent Office
Prior art keywords
well
wells
group
stuck
drill string
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EP89307847A
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German (de)
English (en)
Inventor
W. Brent Hempkins
Wesley E. Lohec
Roger H. Kingsborough
Conroy J. Nini
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Chevron USA Inc
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Chevron Research and Technology Co
Chevron Research Co
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B31/00—Fishing for or freeing objects in boreholes or wells
    • E21B31/035—Fishing for or freeing objects in boreholes or wells controlling differential pipe sticking

Definitions

  • the present invention relates to apparatus for calculating the probability of drill pipe sticking during drilling of a well bore in a given geologic province. More specifically, it relates to apparatus for controlling or modifying drilling conditions in such a well bore to avoid sticking of the drill pipe either due to mechanical conditions affecting movement of a drill string in the well bore, such as high hole angle, oversize drill collars and the like, or due to differential sticking, as a result of excessive differential hydrostatic pressure by drilling fluid in the well bore on the drill pipe against a low-pressure earth formation surrounding the well bore. Other causes of drill string sticking may be further identified and evaluated by the same method, if desired. Further, it particularly relates to a method of and apparatus for determining the probability of freeing such a drill pipe should it become stuck.
  • probability is calculated from a multiplicity of independent and dependent variables which are physical quantities, or parameters, each representing a standard mechanical, chemical or hydraulic drilling condition normally measured or measurable in drilling a well.
  • the same physical parameters in a multiplicity of wells are measured at depths where a drill string has become stuck and at a generally corresponding depth in a multiplicity of similar wells where the drill string has not stuck.
  • the statistical probability of sticking is then calculated by a method of statistical analysis known as "multivariate analysis” from such similarly measured quantities at any single depth in any of such multiplicity of wells in a given geologic province where drill pipe sticking has occurred.
  • "Geological province" includes a geographical area of a geologic region in which a multiplicity of wells have been drilled and wherein similar consequences of earth formations, such as shale-sand bodies or other lithologies of differing compositions, are normally encountered over a range of known well depths.
  • Such measurements are made in wells in which a drill pipe has become stuck in a significant number of instances, due to both mechanical and differential pressure or other conditions in the well bore, and in a similar significant number of wells where the drill pipe did not stick.
  • the probability of avoiding sticking the drill pipe during drilling is increased by selectively controlling the values of such measured quantities or variables in accordance with the coefficients determined for each variable by such analysis.
  • Monitoring and correcting the variable mechanical and hydraulic quantities of such data measured during drilling is accomplished by multivariate analysis of a multiplicity of measured variables in each well of a multiplicity of wells.
  • Such variables are measured in at least two classes or groups of wells, which may comprise wells in which the drill string has stuck and has not stuck, or stuck wells in which the drill string has been freed or not freed.
  • the analysis depends upon matrix algebra to generate a well vector for each well to represent the selected variables of that well at a selected depth over the given depth range. Each such algebraic value is then numerically recorded, or graphically plotted.
  • the plot is within a multi-dimensional space with lower dimensional hyper-surfaces, such as hyperplanes which are selected to best separate distributions of the classes about the mathematical mean or centroid of each of two or more classes of wells from the mean or centroid of the other class or classes.
  • hyperplanes which are selected to best separate distributions of the classes about the mathematical mean or centroid of each of two or more classes of wells from the mean or centroid of the other class or classes.
  • the statistical probability of the selected multiplicity of related and unrelated (but measured and measurable) variables placing each well in the proper class permits a similar well vector generated from current or proposed drilling conditions in another given well to be determined.
  • the current or proposed position of such a well vector is thus related to the mean or centroid of each group or class of wells.
  • Control of drilling conditions in an individual well may then be evaluated, and if required, modified, by changing variables, such as drilling mud properties, hole angle, drill string composition, etc., to effect positively or negatively the numerical value of the well vector to change its plotted location relative to the spatial areas, or numerical means, representative of the respective classes of wells.
  • variables such as drilling mud properties, hole angle, drill string composition, etc.
  • Multivariate analysis means the application of methods for evaluating the simultaneous relationship of a large number or multiplicity of variables or measurements (well parameters) for each of a multiplicity of objects (drilled or drillable well bores) by the use of data matrices to separate groups or classes of such objects and to properly classify another object into one or more groups of classes.
  • plane (including “mapping plane” or surface) is used to identify any surface or space definable whether two or multi- dimensional and either flat or curvilinear.
  • the term also includes hyperplanes or surfaces in a space or sub-space that is useful for displaying or measuring distances between points of intersection, between such plane or surface and a well vector. More specifically, a plane is any space or surface such that the tangent to that surface is a constant, either scaler or some functional representation thereof.
  • centroid denotes a location in multidimensional space which is a special volume or area containing the central tendency or well vectors comprising a group of well vectors which are sufficiently spaced from the central tendency of any other statistical group of well vectors.
  • well vector is used to indicate the vector solution of all measured or measurable variables in any group or groups of wells analyzed by multivariate analysis or in any other well to be studied.
  • mean relative to well vectors and plane includes any measure of central tendency of numerical values whether, arithmetic, geometric, harmonic or other mathematic forms, including modal tendencies.
  • Arimetic means solutions involving manipulations of numbers or mathematical functions describable by numbers including both real and imaginary numbers which can be manipulated in any spatial context including both Euclidean and non-­Euclidean spatial systems.
  • Coefficient refers to any number or group of numbers which are observed or calculated and whose purpose is to describe or to be used to describe some condition of the arithmetic, mathematical or statistical condition or outcome of such observations or calculations.
  • Correlation means any arithmetic expression involving the relation between one or more sets of observations. This includes all distance relationships in any coordinate system. It includes the Pearson-product-moment correlation coefficient, which is convenient to calculate and has special matrix properties as described herein.
  • Cluster is a way of expressing the central tendency of a multiplicity of measurements in any space (see mean).
  • Covariance in general is an expression of the same relationships as correlation and may indeed be the same. Normally, covariance has dimensions, whereas correlation need not (see correlation).
  • Discriminant includes any devices, either mathematical or graphical, which can be used to separate groups of objects into conjunctive or disjointed "families" of objects with or without probability estimates of group membership. This includes all linear and non-linear and all Euclidean or non-Euclidean spaces.
  • Dispossion is a measure of the distance of an object (well, etc.) from one or more measurements of central tendency (see means or centroids). It may also refer collectively to a group of objects expressed as a scalar vector or matrix value. "Standard deviation” or “Second Moment” are synonymous terms, as is Variance.
  • Eigenvalue is the characteristic or latent root of a vector or matrix. It may be real or imaginary.
  • Eigenvector is a vector, real or imaginary, associated with any eigenvalue. Generally it is a vector with both magnitude and direction. Two or more eigenvectors can determine a plane, generally in multidimensional space and, therefore, a hyperplane in that space or any sub-space thereof.
  • Expert System is any means for minimizing the behavioral skills or expertise of a human by storing knowledge of an expert or experts which allows operation of a process or procedure without such an expert being present to observe or measure variable conditions and respond or act on such observations or measurements.
  • the term is also known as knowledge-based systems, or so-called artificial intelligence.
  • Fractor analysis is an arithmetic, mathematic or geometric means of factoring a set of distance or dispersion measures into correlated or related subsets in which the objects (wells) have the highest relationship possible under the criterion for factoring.
  • factorsing include varimax, minimax, quartimax, maximum or minimum entropy, as well as other methods.
  • geometric is generally, but not inclusive, or numerical descriptions of spatial representations of data or derived spaces, or hyperspaces, or sub-spaces thereof.
  • hyperplane is any surface expressible by the numerical, arithmetic, geometric or other operation which describes any space or sub-space, including graphical means or projective geometry.
  • word plane will be used to describe a hyperplane.
  • linear has several definitions. In one sense, linear may refer to a simple arithmetic computation using any ordinary mathematical method. It may also imply a straight line or simple flat surfaces known generally as a plane; however, such lines or surfaces may be curved but defined by techniques generally known as linear transformations. However, the term linear also implies in differential mathematics that derivatives of any relationships are also linear. For example, while logarithms, exponentials, etc. are not linear, under normal circumstances their derivatives may be used to linearize their transformations. Linear may also include non-linear spaces, variables or transformations thereof.
  • Linear Program means any optimization technique which is designed to minimize or maximize some objective function, often called a "cost" function, such that analysis results can be designed to yield solutions which optimize program results.
  • cost means any optimization technique which is designed to minimize or maximize some objective function, often called a "cost" function, such that analysis results can be designed to yield solutions which optimize program results.
  • linear programming includes such non-linear approaches as quadratic or dynamic programming and mixed integer techniques as well as any other optimizing methods.
  • map refers to any mathematical, algebraic or projective means of portraying the spatial characteristics of any observed or derived solutions in any space.
  • Microx means any arrangement of observed or calculated numbers which can be arranged in any sort of array comprised of rows and columns in any sort of space, including linear or non-linear spaces.
  • Some "matrices” have special properties. We here include the word matrix to pertain to such normally recognized spaces as Hilbert, Euclidean, linear, non-­linear, Positive, Definite, semi-Definite, etc., as may be applied to the problem.
  • Multidimensional as used herein includes the concept of one or more observed or collected variables and all results coming from manipulation of the variables and their derivative arithmetic or geometric operations, including any graphic representations.
  • Mean refers to any measure of the central tendency of a set of observations. Ordinary arithmetic operations can yield the simple arithmetic mean, median, mode, etc. Furthermore, there are harmonic, geometric, etc., means. As used herein, “mean” includes any measure of such a central tendency.
  • Projection is any mathematical or graphical representation of observed or calculated data which shows the results as coincident with any surface or sub-surface in any space either by the solution or an extension of the solution values.
  • Drilling deep wells is a difficult and long-standing problem.
  • numerous deep wells are usually drilled from a single stationary platform within a work area generally less than 1/4 acre.
  • the wells must be directionally drilled ("whip-stocked” or “jet deflected") at relatively high angles from vertical to reach substantial distances away from the single platform.
  • petroleum may be produced from formations covering substantial underground areas including multiple producing intervals.
  • water-based drilling fluid to lubricate and flush rotary drill bit cuttings from the bore hole, but more particularly, it provides hydrostatic pressure or "head" in the well bore to control higher pressures that may be encountered in a petroleum-containing formation cut and traversed by the hole.
  • hydrostatic head prevents "blow-out” or loss of gas or oil into the well during drilling.
  • the drilling fluid contains solid materials that form a mud cake on the wall of the well bore to seal any permeable formation traversed by the well during deeper drilling.
  • Such water-based drilling fluids, including sea water are substantially cheaper than the alternative oil-based fluids from the standpoint of original cost, maintenance, formation evaluation, and protecting the ocean environment.
  • oil-based or chemically-­based fluids are useful in certain environments and accordingly are frequently used.
  • This condition may occur in the drill collar section of the drill string which is used to apply weight to the bit directly above the drill bit, but apparently more frequently, occurs at shallower depths where return mud flow around the smaller diameter drill string is less turbulent and hence relatively laminar.
  • higher differential pressure across the drill pipe increases its adherence to the side of the well bore. In a worst case, this results in differential-­pressure sticking of the drill string.
  • the counterbalancing hydrostatic pressure in the well cannot be reduced safely at either the shallower or deeper depths.
  • increased pressures on the low pressure formations may substantially increase the risk of fracturing such a formation, with accompanying loss of liquid from the drilling fluid in the well into the fracture and increase in filter cake around the drill string.
  • lubricating fluid may be "spotted" in the well at or near the drill string sticking point to reduce friction between the drill pipe and the filter cake or to displace drilling fluid in the annulus between the bore hole and the drill pipe.
  • water based fluid, oil, or oil based fluid may assist in breaking down the filter cake so that adhesion of the drill pipe to the well bore is reduced enough to free it.
  • a drill string may stick in a drilling well because of mechanical problems between the drill string and the well bore itself. Such a condition can sometimes occur by what is known as the "keyseat effect". That is, a keyseat is created when the drill string collar or a pipe joint erodes a circular slot near the size of the outside diameter of the collar or tool joint in one side of the larger circular bore hole, as originally cut by the drill bit. Such a slot can create greatly increased friction or drag between the drill string and the earth formation and result in seizure of the drill collars when an attempt is made to pull the string out of the hole and the collars become wedged in the keyseat.
  • Such problems can also be created by excessive weight on the drill string so that the drill string buckles in the lower section and particularly where the bore hole is at a high angle, say in excess of 60° from vertical, or the well bore includes more than one change of direction, such as an S-curve or forms one or more "dog-legs" between the drilling platform and the drill bit. It is also known that in mechanical sticking of a drill string, earth formations around the well may be sufficiently unstable so that the side wall collapses into the well bore and thereby sticks the pipe.
  • MWD Measure While Drilling
  • many formation evaluation, or well logging, tools such as resistivity and self-­potential measuring devices depend upon the use of water-based drilling fluids because such fluids are electrically conductive through the earth formation, rather than insulative, as in the case of oil-based drilling fluids.
  • the formation evaluation tools such as radioactive tools
  • the cost of preventing pollution of water by oil based fluids can be exorbitant due to the high cost of transportation of spent fluids and cuttings to land-based toxic waste dumps, as compared to conventional drilling systems. Accordingly, it is highly desirable, if at all possible, to drill with conventional water-based drilling fluids while still avoiding drill pipe sticking.
  • U.S. Patent 4,428,441 - Dellinger proposes the use of non-circular or square tool joints or drill collars, particularly in the drill string directly above the drill bit. Such shape assures that circulation is maintained around the drill pipe and reduces the sealing area between the pipe and the side wall where the differential pressure may act.
  • tools are expensive and not commonly available. Further they may tend to aggravate the keyseat problem in relatively soft formations since the square edges of such collars may tend to cut the side wall in high angle holes.
  • U.S. Patent 4,298,078 - Lawrence proposes using a special drill section directly above the drill bit to permit jarring the drill bit if the pipe tends to stick. Additionally, valves in the tool may be actuated to release drilling fluid around the drill string to assist in preventing or relieving stuck drill string condition.
  • U.S. Patent 4,427,080 - Steiger is directed to binding a porous layer on the outside of the drill string. Such a coating is stated to prevent differential pressure sticking of the pipe by increasing liquid flow around the drill string.
  • U.S. Patent 4,423,791 - Moses discloses avoiding differential sticking by use of glass beads in the drilling fluid to inhibit formation of a seal by the filter cake between the drill string and the well bore adjacent to a low pressure zone.
  • the present invention is particularly directed to a method of, and apparatus for, evaluating the probability of correctly classifying the current or expected status of a well being drilled, or to be drilled in a known geologic province (as discussed above) without precise knowledge of the formations to be encountered. Then, based upon the evaluated probabilities, any selected one or more of a multiplicity of variable conditions or quantities that affect drilling fluid physical and chemical properties, drill string configuration, and bore hole characteristics in earth formations traversed by the well bore may be controlled to correct or maintain drilling conditions in a well to minimize the probability of sticking the drill string. Moreover, if a drill string becomes stuck, the probability of the cause for such sticking may be identified so that efforts to divert or relieve the drill string may be optimally directed to modify proper variables in an amount and in a direction to relieve such sticking.
  • the present invention may be used to control drilling either continuously or periodically over given depths of a well, and if the drill string sticks, to evaluate not only the probability of how it became stuck, but also to determine whether to begin or continue fishing or other remedial action, or to abandon further efforts to relieve the drill string.
  • statistical control of drilling is carried out by collecting and recording measurements in an adequate number of types of wells where the drill string has, or has not, stuck in a given geological province.
  • a data base, or file is first collected by a storage means and assembled to form data matrices containing a multiplicity of values for each well, based on drill string and bore hole parameters at a selected well depth in each well.
  • Such data matrices are then assembled in groups or classes of wells which include a multiplicity of wells in which the drill string has become stuck and another multiplicity of wells that have been drilled, without becoming stuck, through a depth interval comparable to wells in the stuck class.
  • a data matrix is similarly formed of all wells and their measured values.
  • a probability "map” is created by plotting or recording a vector, referred to herein as a "well vector", based upon manipulation of the data matrices for each variable in each well of the matrices, or another well in the same or a similar geological province.
  • each measured variable in each group of wells is an element, xji, (column or row) of an array.
  • the size or order of each such array, or matrix is equal to the selected number of variables M recorded in each well and the complemen­tary column or row of the selected number N of wells.
  • a variance-covariance (or other equivalent measure of similarity) matrix for each such variable relative to other variables in all other wells of its class, or group is developed.
  • the Pearson-product-moment correlation coefficient, or any similar measure of similarity/dissimilarity, matrix for each class of wells is developed.
  • the eigenvalues and eigenvectors (or other characteristic functions) of these similarity/disimilarity matrices are resolved. In most cases, the eigenvalue and eigenvectors then determine the relative contribution or coefficient of each of the multiplicity of measured variables in the pertinent matrices.
  • Such coefficients provide well vectors falling into substantially distinct groups that statistically group about a central cluster or tendency, call the mean or centroid, for wells in each group.
  • Such groups are spatially separable and recordable for graphic display, or analysis, of any well vector relative to all wells sampled in a given geological province.
  • the coordinates of a well vector in any hyperspace are established by multiplying each measured variable by the coefficient value for that variable and summing such values to locate the intersection of the data vector in that hyperspace or sub-space.
  • multivariate discriminant analysis of the data matrices includes recording the data and graphically finding a mathematical hyperplane which optimally separates two groups. Where three groups are evaluated, the third group is separated by another hyperplane so that two hyperplanes separate the three groups. In this case the three groups may then be plotted as projections onto a surface representing the two hyperplanes. Each well vector representing one of the wells in the complete set of wells may thus be projected onto the plotting surface as a point. From these points the intergroup distances between a well in any group and the centroid of each group may be calculated. The grand centroid of all such values may also be determined, and, if desired, recorded for mapping or plotting on the plotting surface.
  • the probabilities of correctness may be contoured in any sub-space. Where the probabilities are nearly equal that a well belongs to either of two groups, the vector intersec­tion point will normally fall near the intersection of the hyperplanes or plotting surfaces and the area between the centroids for each group will be approximately equal. Accordingly, the further the well vector point is removed from such an intersection toward one of the group centroids, the greater the probability that the well is correctly classified.
  • the highest probability that a well is correctly classified is where the normalized or scaled distance of a well vector to the total within-group or grand well centroid is a maximum and the distance of the well vector away from all other groups centroids is a maximum. Such values or distances may be expressed either numerically or graphically.
  • the multiple measured parameters at any depth in each well adequately and clearly delineate the probability that during drilling of any well within the sampled depth interval will fall into the correct category of any two or more groups, such a well to be drilled, or being drill, may be controlled to "steer" its drilling conditions away from any sticking hazard and toward the probability of not sticking the drill string.
  • data storage or file means are provided for storing each individually measured value of a multiplicity of controllable well drilling variables or parameters in each of a multiplicity of individual well bores in a geological province wherein drill strings have been stuck.
  • the selected depth for such measurements is preferably the depth where the drill pipe actually stuck, but may include the history of conditions prior to sticking, including depth.
  • one depth is randomly selected within the range of the depths where the drill string stuck in other matrix wells.
  • Each measured value stored in the data file means then forms input to means for converting such stored values into elements of matrix arrays. These arrays correspond, for example, to all wells in the total group, a stuck well group, and a not-stuck well group.
  • a linear or other algebraic and statistical proposition is defined for two groups of wells or more. It is particularly convenient to analyze only two or three groups in the multidimensional space of this problem, but greater numbers of groups may be analyzed without departing from the scope of the present disclosure.
  • the eigenvector coefficient solutions of these matrices is then computed by discriminant analysis means so that such coefficients maximize the determinant of the ratio of the distances of each group of well vector between-­group centroids to the distances of well vectors within a group centroid, for all wells in the stuck and not-stuck groups.
  • the three matrices are formed by means for calculating the mean corrected sum-of-squares of the cross-products for the "scores" of all stored values in each matrix.
  • the latter is obtained by means for cross multiplication of the corresponding measured variables and addition of their cross products for all possible pairs of well in each matrix and scaling by the appropriate standard deviations.
  • a well vector for each well in the total group may then be generated.
  • Such vector is generated by means for calculating the sums of the products of the value of each recorded parameter multiplied by each eigenvector coefficient.
  • a multiplicity of well vectors from the multiplicity of wells is stored by recording means and, if desired, indicated by plotting means to display a probability map applicable to the entire geological province. Points plotted on the map may be displayed as points about the centroids of points which represent wells that are (1) known to have stuck either (a) by differential pressure, or (b) because of mechanical problems and (2) wells where the drill string did not stick.
  • Each of the three groups may be similarly separated by a technique known in statistics as "multivariate analysis".
  • the three groups are separated by intersecting mathematical hyperplanes.
  • Each well vector from multidimensional space is then resolved into a set of coefficients representable as a point on a mapping surface. This permits vector projections from multidimensional space to be separated to the maximum extent and the vectors plotted.
  • contouring the probability of each well as represented by its vector coefficients on the mapping surface it is thereby possible to separate probablistically wells that become differentially stuck from those in which the drill string become mechanically stuck, and both are separated from the non-stuck drill string vectors.
  • the coefficients for each such variable may be used to calculate the predicted well vector, which is the sum of each coefficient multiplied by its corresponding, currently measured variable value. These sums yield the vector coordinates of the well vector being controlled for display on the mapping surface. The probability that the present position of the drilling well vector will or will not result in the drill string becoming stuck may then be evaluated by its distance from the centroids of the three groups.
  • controllable variables e.g., mud weight, solids, drill collar size, etc.,
  • the controllable variables e.g., mud weight, solids, drill collar size, etc.,
  • the controllable variables may be evaluated and modified to move the probability of the drilling well toward the coordinates of the map that represent a desired high probability that the well is in the "not stuck" region.
  • Such a procedure makes possible analysis and directional control of the drilling well to preclude the probability of sticking the drill pipe in a drilling well.
  • multivariate analysis may be similarly used to evaluate the probability that a stuck drill string can be freed.
  • a plurality of wells in which the drill string has stuck forms the total group in which a multiplicity of drilling parameters are recorded for each well at a given depth.
  • the given depth as above-discussed, may be either at the depth where the drill string became stuck or at another depth near the sticking level.
  • Wells which were then subsequently freed and those which were not freed are then grouped together for the multivariate analysis. Accordingly, group matrices are formed and calculated to determine the variance-covariance, or similar measure of similarity of, distance of each well vector to the group centroids or means.
  • the eigenvector coefficients that minimize the ratio of (a) the distance of a well vector to its within group centroid to (b) the distance between group centroids are scalar.
  • the wells in which the drill string could not be freed cluster about their centroid and are clearly distinguishable by such scalar values, from the centroid of wells where the drill string was freed.
  • the multiplicity of measurable values may include time values, or other variables based on when a drill pipe becomes stuck, such as time elapsed between sticking and "spotting" oil in the stuck well.
  • the method of determining the probability of releasing or freeing a stuck drill string may be carried out either independent of well drilling control or other forecasting aspects of the present invention.
  • the apparatus or method aspects of predicting whether the drill string can be freed may use substantially the same multiplicity of well variables.
  • Fig. 1 indicates in elevation and partially in perspective, a fixed off-shore drilling platform 10 of the type normally used to develop a major portion of one or more underwater producing forma­tions.
  • the well drilling control system of the present invention is particularly applicable to such drilling because a plurality, say 10 to 30 wells such as 11, 12, 13, and 14 and 15 are drilled from a single platform 10 at high deflection angles to vertical to develop an underwater petroleum reservoir 16 extending over several thousand feet laterally from the platform.
  • the wells 11 to 15 are selectively drilled at differing angles and may include one or more "dogs legs" 18 (different angles to vertical). They may even take S-curve configura­tions, as in well 11 or 14, in drilling to a desired depth.
  • Such configurations may either be planned because of geological conditions or occur inadvertently during drilling.
  • Normal well pressure is essentially the pressure of fluid in a well bore at a given depth.
  • the well pressure as applied by the density of the drilling fluid, or mud, in the hole, must exceed pressure in the formation.
  • formation pressures may be nearer to normal for such depth. Accordingly, to maintain adequate well pressure opposite the upper high-pressure formation, hydrostatic pressure on the lower formations may be excessive. Such excessive well pressure may fracture the formation, with resulting loss of drilling fluid to the formation and consequent blow-out danger.
  • the thixotropic drilling fluid returning to the surface from the drill bit and flowing over the remaining area of the bore hole 21 may become relatively laminar so that the fluid tends to set up or gel.
  • the precise cause of such differential sticking is frequently difficult to determine. Hence, correcting such a condition is, in general, by trial and error.
  • the prospect for correcting a stuck condition may determine how much non-drilling rig time the operator can afford to use in "fishing" or other operations, as opposed to the cost of abandoning that portion of the well bore.
  • Such abandonment frequently requires sidetracking the hole above the last pipe section that is not stuck. This may require explosively cutting or unthreading the drill pipe above the stuck point and perhaps setting a plug in the bore hole, with consequent loss of equipment including drill collars and bit, or other devices.
  • the well is then redrilled to the same depth, and deeper if possible. Accordingly, knowing the probability of avoiding sticking or unsticking a differentially stuck drill string, as well as knowing the probability that the drill string is mechanically stuck, rather than differentially stuck are of high economic value. This is particularly true where rig cost is on the order of thousands of dollars per hour, as in offshore drilling.
  • Figs. 2 and 4 illustrate a portion of a drill pipe 17 above the drill collars 25 and drill bit 27.
  • substantially all of the drill pipe 17 is smaller in diameter than bore hole 21, as originally cut by drill bit 27.
  • the drill pipe proper is more flexible than the bottom hole assembly, including drill collars 25 and drill bit 27. Accordingly at high angles, the drill pipe may tend to sag against one side of the well bore wall.
  • the drill string in such a condition may mechanically cut the side of the well bore as at 29 in Fig. 2 and 4 to form what is known as a "keyseat". Under such conditions, the diameter of drill pipe 17, or joints between pipe sections are smaller than the drill collar sections or drill bit.
  • the pipe or joints may cause the pipe to mechanically stick in the bore hole.
  • Figs. 5, 6 and 7 show in bar graph form the percent of wells in the sampled number where pipe became stuck mechanically or differentially over a range of from 0° to 75° deviation from vertical.
  • Fig. 6 indicates, in a bar graph, the distribution of the three classes of wells forming the data matrices plotted as a function of depths of the wells.
  • Fig. 7 is a bar graph of the hole size range of wells in the sample of stuck wells.
  • Figs. 8, 9 and 10 are probability plots of well vectors (eigenvector projections) onto hyperplanes or maps of each well in each of the three classes of wells. These plots or maps were developed by mutlivariate analyses of measured variables in each of the three classes. These maps indicate that the three classes of wells can be readily distinguished with sufficiently high probability so that, by measuring the same multiplicity of measured variables at any depth in a well being drilling, the drilling conditions in the well may be plotted to control the well while it is being drilled. Such control may be either by preplanning and drilling program or by implementing corrective action during drilling. A typical well during drilling is plotted, as in Fig. 10, to show its progress, relative to the three conditions, on such a two-dimensional map, based on projections of the well vector groups to the best separating hyperplanes.
  • Discriminant analysis is used to determine whether groups with different predetermined attributes (i.e., mechanically stuck, differentially stuck, and non-­stuck wells) can actually be distinguished as such, on the basis of data from each group.
  • a discriminant analysis involves the solution of a set of equations that are derived from matrices describing the "between-group distances" and the "within-group distances" of all wells in a particular group about their joint means (centroids) and about the grand joint mean, or grand centroid.
  • Fig. 8 is illustrative of separation of three groups by two intersecting hyperplanes, as indicated by three lines intersecting at the center of the plot. These lines are the best separating boundaries, as determined by such hyperplanes, here shown as linear.
  • Fig. 9 is similar to Fig. 8 and illustrates so-called "iso-probability" contour lines in each of the three groups indicating the probability that each well vector is correctly plotted within the assigned group.
  • the well plotted in Fig. 10 is on the same vector coefficient map as the wells plotted in Figs. 8 and 9.
  • Fig. 11 illustrates in a triangular graph an alternative method of plotting the probability data of the wells shown in Fig. 9 for each of the three classes of wells.
  • the nearer each well is to the probability apex (100%) in each class the greater the probability that it is correctly classified.
  • the corrective action can be undertaken through modification of the most significant contributing variables.
  • a total of 82 wells were plotted, 36 wells stuck the drill pipe differentially, 33 did not stick the drill pipe, and 13 stuck the drill string mechanically.
  • 59 were plottable within 90% probability that they were correctly predicted; that is, within the shaded triangular areas at the three corners of Fig. 11; the remaining well vectors were then plotted as indicated by the individual well vector plots for 5 mechanically stuck, 10 differentially stuck, and 8 not-stuck, wells.
  • the distances between the group and grand centroids and the distance of a given well vector to each of the centroids may be recorded and indicated as distance values alone, with plotting or mapping a graphic alternative only.
  • multivariate analysis of a multiplicity of well parameters in a multiplicity of wells may be similarly used to evaluate the probability that a drill string that became stuck by differential pressure can be freed.
  • Fig. 13 illustrates in bar graph or histogram form such analysis of 43 wells in which the drill string had stuck. In all, 23 wells of the total group were freed and 20 were not freed.
  • the multiplicity of drilling parameters recorded for each well at a given depth may be the same as those used to determine probability of the drill string sticking as described in detail above.
  • the analysis additionally includes a variable that reflects the time interval from drill string sticking to "spotting" of oil in the well to break the filter cake between the bore hole and a porous formation.
  • such oil is normally added to the drilling fluid when the drill string becomes stuck to lubricate the bore hole or to break the filter cake.
  • the well vectors for each of the freed and not freed wells are plotted. Wells which were subsequently freed and those which were not freed are seen to cluster together by such multivariate analysis. Because only two group matrices are formed, determination of the variance-covariance of well vectors for each well relative to the centroids of the two groups need be only scalar (one dimensional) values.
  • centroid of 0.54 may be chosen as the average value of the probability that a drill string can be freed.
  • Fig. 13 indicates that wells that could not be freed are particularly clustered about their well vector centroid and are best separated from the freed well vector centroid by a value of about 0.60. This gives a well operator strong indications, in early efforts to free the drill string, as to the amount of time and cost that should be incurred before abandoning or sidetracking the well. Because each fishing job in a deep well may cost on the order of $500,000 or more (primarily based on lost time in drilling), such probability determination can save substantial drilling expense.
  • the individual historical cost items of stuck wells may also be used as additional variables in matrix analysis of these wells.
  • a system can be implemented as shown in Figs. 14 and 15.
  • well data accumulated in unit 50 flows into automatic data processing means 52 to generate the required sequence of events to analyze the multiplicity of measured variables in a multiplicity of wells to generate well vectors useful to control selected well variables for continued drilling of a well or to generate displays of probabilities if drilling is continued using selected values of such variables.
  • Fig. 14 particularly illustrates the general system which includes means for accumulating well data, as in Well Data Storage file or means 50.
  • the basic well data comprises a multiplicity of values or parameters of measured drilling variables M for each of a multiplicity of wells N.
  • the data may be taken from previously drilled wells, or on a cumulative basis over a period of time, as by frequent, say daily, entry of values from individual wells. Entries of data may either be key-stroked into well data, or file storage, 50 or introduced as a stream of data via modem, tape, disc, or computer from the "tour" sheets or daily logs of each well.
  • Stored data in well data file 50 is then fed to Matrix Converter 55 of data processing means 52.
  • processing means 52 The major elements of processing means 52 are Multivariate Analysis means 56 and Well Vector Generator means 58.
  • Matrix Converter 55 establishes a grand or total matrix of all values of the measured well parameters for each well, and at least two additional matrices formed by classes, or sub-groups, of the total group of wells.
  • Multivariate Analysis means 56 then resolves each matrix into a set of coefficients representing each variable of the matrix.
  • the coefficients are stored in Well Vector Generator 58 to generate an individual well vector for each well in the total matrix from the stored variable values in Well Data Storage means 50.
  • the same variables for any other well or group of wells or sets of such variables from simulated or actual well drilling conditions may be similarly computed by feeding such sets of data from Test Well Unit 100 to Well Vector Generator 58 through input line 57.
  • each well variable is multiplied by its corresponding coefficient and the products are summed.
  • the centroids of each group of well vectors and the total group are computed and their relative distances determined and their values stored in Well Vector and Centroid Recorder 60.
  • Such recorded values preferably include the relative distances between individual well vectors, the distance of individual well vectors to their respective within group centroid, as well as their relative distance to other group centroids and the grand centroid.
  • These recorded values are then available for display and analysis by Display unit 62.
  • Unit 62 may either present the recorded values as a map, as shown in Figs. 8 to 13, or as numerical values alone.
  • Multivariate Analysis means 56 includes Matrix Converter 55 which is supplied with a multiplicity of variables M from each of a multiplicity of wells N for each group of wells or the total wells from Data Storage means 54. Converter 55 then selectively forms a matrix for each desired group or class of wells using the same multiplicity of well drilling variables for each well in its respective group.
  • each matrix formed in Matrix Converter 55 may be stored in registers or compilers indicated as Total Well Matrix register 63, Non-stuck Well Matrix register 64, Stuck Well Matrix register 65, Mechanically Stuck Well Matrix register 66, Differentially Stuck Well Matrix register 67, Freed Well Matrix register 68, and Non-­Freed Well Matrix register 69. It will be understood, of course, that the matrix in each of registers 63 to 69 is selectively connectable to the multivariate analysis computation apparatus for serial processing through Standard Mean Vector Computer 70, Standard Variance Vector Computer 72 and Variance-Covariance Matrix Computer 76.
  • a matrix stored in one of the registers 63 through 69 is fed to Standard Mean Vector Computer 70 for computation of the values of X i .
  • the output of computer 70 is supplied to the Standard Variance Computer 72 for calculation of S i and then to Variance-Covariance Matrix Computer 76.
  • such computation includes formation of a Pearson-product correlation matrix, or another similarity matrix for the computation of C ik .
  • each of the matrices stored in the Non-Stuck Well register 64 and Stuck Well register 65 may be separately computed and compiled to form values stored in the Total Group Correlation Matrix, T, unit 78, preferably the total group correlation matrix is separately computed from total well data stored in register 63.
  • Such total well data may be formed by any desired groups of well data, and need not contain all groups where fewer groups, such as where Freed and Not-Freed Well Computers are to be separately evaluated.
  • the within group correlations are then calculated from the output of Variance-­Covariance Matrix Computer 76 in Within Group Correlation Matrix Computer 80 to determine the value of W.
  • Test Well Data unit 100 Data from any subsequent well, indicated as being stored in Test Well Data unit 100, will include the individual M variables for that well. Thus, data from wells in the same or a similar geological province may be evaluated, whether actual measurements or simulated data.
  • Well Vector Generator 58 multiplies and sums the total of each eigenvector times its corresponding variable delivered from either data storage 54 or test well data in unit 100.
  • Individual well vectors are stored in Well Vector Register 92 of Recorder 60. These may be directly displayed as by Display unit 62 or used to compute group centroids or a total group centroid in Group Centroid Generator and Register 94.
  • a grand mean, or grand centroid, and group centroids for each selected groups of wells will be recorded as plotting data for display on a surface.
  • the data will include relative distances between group centroids, and their distance to the grand centroid.
  • distance from any selected well vector to any group centroid may be calculated and stored in Distance Indicator 96 for read-out or display as numbers. Display of the complete suite of well vectors, or any selected summations or parts, may then be plotted as by Centroid, Well Vector and Probability Plotter 98.
  • a simple distance indication is adequate to distinguish a well selected from data storage unit 54, or a test well, relative to the centroids of the two groups.
  • such display may be suitable for a system such as that described in connection with Fig. 13 for distinguishing between freed wells and not freed wells after a well has become stuck.
  • multiple groups are to be displayed as in Figs. 8 to 12
  • plotting of the centroids and an individual well is preferred, and, preferably probability contours, as indicated in Fig. 8, may be added to the display prepared by the plotter.
  • a single distance indicator for a test well relative to any of the three groups may be quite sufficient for display.
  • the distance may be expressed either as a ratio of numbers, or as a number indicative of relative distances, without actual display of the data on a map, or other graphic plot.
  • Fig. 15 also indicates means for optimizing variable values that can be selected within given ranges of values to correct or avoid the probability of sticking a drill pipe in a well under given conditions in the test well.
  • Data stored in unit 100 may be diverted to Linear Program (LP) Variable Ranges unit 102 for successively supplying physically feasible values for each selectable variable to Well Vector Generator 59.
  • LP Linear Program
  • the resultant values are then recorded in Register 92 and supplied to LP Optimizer 104 along with centroid data from Centroid Generator and Register 94.
  • Optimizer 104 is set to terminate operation of unit 102, as by switch means 106, when a selected value, or a selected value within a range of values, optimally moves the predicted well vector away from the stuck well centroid or centroids and as near the non-stuck well centroid as economically feasible.
  • steps include calculating the probabilities of sticking the drill pipe and avoiding sticking while drilling a well bore with water-based or other drilling fluids.
  • steps are as follows:
  • Selection of the wells for identification in each of the three groups is made on the basis of the values measured for the set of variables at a selected depth in each well.
  • the values in the set for stuck drill strings are preferably the last values measured at substantially the depth at which each drill string became stuck, mechanically or differentially.
  • values of the variables measured in a stuck well just before the drill string became stuck may also be used.
  • a single set of variables for each non-­stuck well is selected at a randomly chosen depth within a typical range of depths of the differentially and mechanically stuck wells.
  • the variance for each column is calculated as follows:
  • the variance S i is calculated as: (as used in the following tables, 62,500 in computer "floating point" notation is 0.625 X 105 and expressed as 0.625E+05
  • the VARIANCE/COVARIANCE MATRIX is then formed.
  • the VARIANCE-COVARIANCE MATRIX for the first group of wells is then: When the diagonal entries are divided by the variance of that variable and when the off-diagonal elements are divided by the product of the standard deviations of the variables represented by that row-column intersection, (e.g., the covariance for the variables at the row one intersection with column two is divided by the standard deviations of variable 1 and variable 2), the resulting matrix is the Within Group Correlation Matrix.
  • this matrix is symmetrical about the diagonal, e.g., the intersection of row 1 with column 2 is the same as the intersection of row 2 with column 1. (That is, the correlation of the i th to k th variable is the same as the correlation of the k th to i th variable).
  • the Within Group Correlation Matrix has the special properties that it is square, positive, and semidefinite (i.e. all its characteris­tic roots are non-negative).
  • the eigenvectors define the discriminant functions when properly normalized.
  • This example of a simplified computation of a 2x2 matrix example does not have the same properties of the correlation matrix, as one of the eigenvalues in this example is negative.
  • such example was selected to illustrate the computation and, because even a simple matrix, as presented in the previous example of 3 groups, is somewhat too complex to be readily solved by a hand calculator.
  • the 3x4 matrix of the example may be solved by a program such as those of SAS (Statistical Analysis System, SAS Institute, Raleigh, N.C.), or BMDP4, Los Angeles, California.
  • H2 is the null hypothesis that no relationships exist.
  • the null hypothesis was rejected at the .01 probability level, i.e., there is only 1 chance in 100 that the calculated results could have arisen by chance.
  • Each well's discriminant "score" is calculated by multiplying the original values of each variable for the well by the corresponding discriminant coefficient pertaining to each variable and then summing the results for the four variables for each well in each group in the above example are: ORIGINAL VALUE OF VARIABLES TIMES EIGENVECTORS FIRST GROUP OF WELLS Wells 1 2 1 35.37 -3.14 2 34.91 -3.38 3 34.80 -2.86 ORIGINAL VARIABLES TIMES EIGENVECTORS SECOND GROUP OF WELLS Wells 1 2 4 21.40 -2.60 5 19.70 -2.71 6 20.25 -3.92 ORIGINAL VARIABLES TIMES EIGENVECTOR THIRD GROUP OF WELLS Wells 1 2 7 25.00 -3.44 8 26.68 -3.15 9 27.24 -3.89 This essentially completes the discriminant analysis.
  • results for each well in each of the three groups of wells may then be plotted as either a two dimensional plot, as in Figures 8-10, or in a triangular form, as in Figure 12 or in any other convenient numeric or graphic presentation.
  • the probabilities of correct classification can be calculated by conventional methods of statistical analysis using, for example, Chi-squared values, as follows: Using a Chi-squared approximation to a Bayesian statistic the probabilities are found.
  • Multivariate analysis can also be used to determine the probability of so identifying such groups without specific information as to the cause of sticking or whether any of the total group of wells in fact stuck.
  • Q- mode analysis as distinguished from R- mode analysis as described above.
  • the variance covariance or Pearson-product correlation matrix is formed by summing the cross products of each well summed over the number of variables M.
  • Cj q where j, q refers to 1 to N wells is determined, rather than C ik , which refers to 1 to M variables, as above.
  • the covariance is determined for all wells across all variables and all other statistics may be calculated according to the above for all matrix indices. Accordingly, if the number of wells is, say 1,200, the matrix would be 1,200 x 1,200 rather than 20 x 20.
  • the eigenvalue and eigenvector vector solution of such a matrix similarly generates variable vectors which will cluster about centroids representative of variables which can then be inferred to represent different classes of stuck wells. If these classes exist, an R- mode analysis as taught herein can be guided as to probable causes of drill string sticking.
  • each dimensionless matrix coefficient can be calculated with an HP35 (Hewlett Packard) hand-held computer or similar device for a few variables and wells.
  • HP35 Hewlett Packard
  • a program known as SAS available from SAS Institute, Raleigh, NC, will perform statistical analysis as above described either on a large main frame computer or on a personal computer (PC).
  • PC personal computer
  • Such program is capable of performing all steps of multivariate analysis, including matrix computation of principal components, factor, regression and discriminant analysis.
  • W.W Cooley and P.R. Lohnes "Multivariate Procedures for the Behavioral Sciences", John Wiley and Sons, New York,NY, 1962 presents FORTRAN code for statistical analysis.
  • the graphic presentation of the three classes of wells and location of each well vector may be plotted using a program known as Lotus 1-2-3 available commercially from Lotus Development, Cambridge, MA. It can be used together with a program known as dBASE III, available from Ashton Tate, Culver City, CA to manage the data file.
  • Linear programs for calculating each individual well vector to plot and control a drilling well can be performed by a program known as OMNI, available from Haverly Systems, Inc., Danville, NJ. or by a Program MPSX, available from IBM Corp., White Plains, NY may also be used.
  • the "Overall” column refers to movement of a well vector from one location to another on the plot or map.
  • the "Stuck vs Not Stuck” column indicates the relative import­ance of modifying a measured variable to move from a Stuck well (Differential or Mechanical) area toward the Not-Stuck centroid.
  • the "Mech. vs. Diff.” column indicates the relative importance of each measured variable as between the position of a well vector in the Mechanically stuck class rather than Differentially stuck class.
  • the method is clearly applicable to separate into such wells only two groups, such as wells wherein the drill string stuck in all of one group and did not stick in the other group.
  • Such two groups may comprise all stuck wells, which are then used to identify those wells freed and those not freed.
  • the analysis is applicable to distinguish only mechanical sticking from differential sticking. Corrective action for the measured variables, as each simultaneously contributes to the well vector at a particular depth, as related to the entire set of wells, is related to the individual coefficients for each variable.
  • optimal values of the variables to move the well vector into the non-stuck region may be calculated using a linear program (LP) or other optimization methods.
  • the LP using reasonably constrained values for the given well, mud type, and hole conditions, calculates the extent of changes in the variables of the discriminant equation required to achieve the goal of collectively changing the variables to reach or approach the centroid of the not stuck wells.
  • an LP does not necessarily change the variable in a manner consistent with common sense, it may be necessary to constrain the variables within the LP to maintain reasonable engineering values.
  • constraints relation some of the variables, and boundary constraints to keep the variables within reasonable limits.
  • the functional constraints may be:
  • Boundary conditions or constraints are then set for the minimum and maximum value of each of the variables.
  • Target location coordinates in the non-stuck region are then also assigned and equated to the discriminant functions.
  • the matrix is then solved to approach the target discriminant values as closely as possible without violating any of the constraints.
  • the LP optimization system may be used, for example, Ashton Tate's dBase III for the input and output routines and Fortran for the LP matrix solution.
  • Table 3 illustrates an example of the LP input for three groups (two discriminant functions) and twenty variables.
  • the Current Values (Column 2) of the twenty variables (Column 1) are input along with target coordinates.
  • Lower and Upper Limits for the variables (Columns 3 & 4 respectively) are then assigned and an allowable range Down or Up (Columns 5 and 6) of each variable if, indeed any change is possible, are set.
  • an allowable range Down or Up (Columns 5 and 6) of each variable if, indeed any change is possible, are set.
  • limits can also be assigned to the target to allow some leniency in the solution of the matrix.
  • An LP matrix is created and solved by the simplex method, or any other comparable method. Occasionally a solution is not possible within the given boundary conditions and, thus, the constraints on the targets must be relaxed and the LP rerun.
  • An example LP output is shown in Table 4.
  • the proposed LP Values (Column 1) are shown along with the actual Current Values (Column 2).
  • the proposed differences (Column 3) and the new values of the changed variable within the given limits are then shown in Columns 4 and 5.
  • the X and Y target values i.e., the values of the two eigenvector functions) are then plotted relative to the current X and Y coordinates of the drilling well.
  • the user is presented current eigenvector function values at any point in drilling a well bore.
  • the use may select to change some, but perhaps not all, variables (e.g., the well bore shallower). Upper and lower limits are then set on the variables that, practically, can be changed. This then permits plotting the current location on the probability plot and shows the "safe" position to achieve the highest probability of not sticking the drill pipe.
  • Table 4 it will be noted that among significant changes that could be made the operator can increase the mud weight 0.5 lbs/ft3, decrease the drilling fluid water loss 2.3% and decrease the chlorides content of the drilling fluid 2000 ppm. Other modifications such as drill collar diameter (increase) and length (decrease) are as indicated.
  • the well vector of the example well at the indicated depth (450 feet) was, in fact, within the mechanically stuck well group, at a probability of about 90%. Corrections were calculated by the LP to move the well vector into the not-stuck well group, within a probability of about 99%, as may be seen by plotting the starting coordinates and the LP coordinates on the probability map of Fig. 9.
  • geophysical and geological data may be used in a geologic province to further enhance the method of the present invention.
  • clay or shale data including physical and chemical properties, may be used over known depth intervals, particularly where shale is encountered over significant depth intervals.
  • earth formation properties measured by electric, induction or nuclear well logging tools including those measured while drilling, may be used as measured variables in the matrices and their contribution to the discriminant functions used to calculate well vectors representative of drilling conditions in the well bore.
  • the present invention is particularly well suited to utilize well logging data, geological data and drilling conditions detected using down hole measure-while-drilling (MWD) tool assemblies now frequently incorporated into a drill string.
  • MWD down hole measure-while-drilling
  • Such data is particularly desirable in wildcat or step-out wells where such data is not known in detail, or at all.
  • Prompt evaluation of all measured variables, related and unrelated, in a currently drilling well is accordingly available for analysis by the present invention to determine the probability of sticking the drill string and the risk of losing both the well bore and the costly drill string section containing the detecting and measuring systems.
  • this invention represents a substantial economic advantage in avoiding drill string sticking by improving control of well drilling parameters or variables on a continuing basis.

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EP0646881A2 (fr) * 1993-09-30 1995-04-05 Toa Medical Electronics Co., Ltd. Appareillage et méthode pour l'assistance au diagnostic médical par discrimination des degrés d'attribution
EP0646881A3 (fr) * 1993-09-30 1996-03-06 Toa Medical Electronics Appareillage et méthode pour l'assistance au diagnostic médical par discrimination des degrés d'attribution.
US5619990A (en) * 1993-09-30 1997-04-15 Toa Medical Electronics Co., Ltd. Apparatus and method for making a medical diagnosis by discriminating attribution degrees
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NO893113L (no) 1990-02-05
NO893113D0 (no) 1989-08-02
AU3929089A (en) 1990-02-08
DK378289D0 (da) 1989-08-02
CN1016204B (zh) 1992-04-08
CN1040653A (zh) 1990-03-21
DK378289A (da) 1990-02-04
AU621138B2 (en) 1992-03-05

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