CN118246242A - Diversion river system reservoir modeling method based on biased random walk - Google Patents
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Abstract
The invention relates to the field of modeling of oil and gas reservoirs, in particular to a method for modeling a reservoir of a diversion river channel system based on a biased random walk, which comprises the following steps: establishing a well position distribution diagram based on elevation tolerance; establishing an electrostatic field-like map by taking each well point as a charge source point according to the characteristics of an electrostatic field; carrying out biased random walk simulation in the quasi-electrostatic field map according to attraction or repulsion intensity; and establishing a reservoir model of the shunt river channel system based on the simulation result. The invention provides a new method for reservoir modeling of a shunt river system, avoids the problem that a simulated river erroneously penetrates through a non-river well, reflects uncertainty in a probability migration mode, can effectively improve characterization precision and efficiency, and provides important support for oil and gas exploration and development.
Description
Technical Field
The invention relates to modeling of an oil and gas reservoir, in particular to a reservoir modeling method of a diversion river channel system based on a biased random walk.
Background
The split river sand is an excellent carrier of oil and gas resources, the oil and gas total amount of the sand reservoir in China is extremely rich, and a plurality of oil and gas-containing basins are distributed. However, the shunt river sand body has rapid transverse change, the reservoir is heterogeneous and strong, and the efficient continuous development often has certain difficulty. Aiming at the problem, a finer river channel sand modeling and characterization strategy becomes an important solution. However, the continuous improvement of the precision also causes the great increase of the manpower workload, and the higher and higher requirements are put on the manual experience.
For a certain period of secondary stratum, drilling a well meeting a river channel can be called a river channel well; and the non-river well refers to a well which is not drilled to meet a river for a certain period of secondary stratum. With the development of computer technology, various reservoir modeling methods are also becoming mature. The method greatly saves labor cost, is efficient and stable, and becomes an important auxiliary means for fine characterization work. Modeling algorithms are the core of modeling techniques. Among these, various random walk algorithms are attracting more and more attention in the field of river reservoir modeling, especially shunt river reservoir modeling, because of their characteristics of being visual, efficient, easy to generate continuous geologic bodies, convenient for function expansion, and the like. However, the existing random walk model still has some limitations, such as the gridding design is easy to significantly reduce the well position accuracy, and the non-gridded direct connection is easy to connect the non-river well into the walk track.
Therefore, the method for the partial random walk is provided, the technical problems can be effectively solved, and an effective method technology is provided for modeling the reservoir of the shunt river system.
Disclosure of Invention
In order to overcome the technical problems, the invention aims to provide a split-flow river channel system reservoir modeling method based on biased random walk, and the applicant proposes a biased random walk model for errors possibly caused by gridding walk. The model develops the ideas of attraction and repulsion, all well points are regarded as charge source points, the river well is regarded as attraction source, the river well is regarded as repulsion source, the electric field intensity values of all the wells are attenuated according to the distance and then are overlapped in the research range, and an electrostatic field is formed. The electrostatic field is gridded, but the travel track is not limited to any grid. Since the migration probability in a certain direction increases as the electrostatic attraction force increases, the migration probability calculated from the electric field gradient can travel from the position of the river channel well, which is the seed point, without the need to approach the nearby grid point, in combination with the self-avoidance principle. The method not only avoids errors caused by meshing, but also avoids the problem that the simulated river erroneously penetrates through the non-river well, and also reflects uncertainty in the form of probability migration, thereby omitting the addition of disturbance items.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
s1, selecting a stratum of a certain period, determining whether the river channel type of each well belongs to a river channel well or a non-river channel well by a logging identification method, and establishing a well position distribution diagram;
s2, simulating an electrostatic field concept, wherein each well point is used as a charge source point, a river channel point is used as a positive charge source point, a non-river channel point is used as a negative charge source point, and the electric field intensity is attenuated along with the distance and can be overlapped, so that an electrostatic field-like map is established;
S3, starting from a selected river channel well, carrying out biased random walk in an electrostatic field-like map according to attraction or repulsion strength through a biased random walk subroutine, and respectively walking in opposite directions, wherein the walk tracks are connected to obtain a main flow line track of the river channel. Performing random walk simulation in the quasi-electrostatic field map;
S4, traversing all river wells according to the method of S3 to obtain a river main streamline distribution diagram;
s5, changing the river main streamline into a more real river form based on the river configuration parameters on the basis of the main streamline distribution diagram.
In the above steps, establishing an electrostatic field map is a key issue. The electrostatic field map based on river wells and non-river wells is similar to the principle of like-pole repulsion and opposite-pole attraction, and the river wells and the non-river wells generate opposite charges. In electrostatic field-like random walking, walking seeds walk in an electrostatic field with a certain probability distribution. The probability of a seed moving in a particular direction is closely related to the electrostatic attraction force in the corresponding direction. Assuming the walking seed is of negative charge nature, then the river well will be a source of positive charge, whereas the non-river well is a source of negative charge. For electrostatic source points, the electric field strength is very large near the source point and decreases exponentially with increasing distance. The formula is as follows:
；
Wherein I is the electric field strength at a given location; i 0 is the intensity of the electrostatic source point; d is the distance from a given point to the electrostatic source point; ls is the characteristic length. Like a real electrostatic field, the electric field strength here also has the capability of superposition. For all well sites, the superposition constitutes an electrostatic field map.
In step S3, a biased random walk subroutine technical route is shown in fig. 2, and is used for simulating random walk in the virtual environment driven by the electrostatic field-like map data. The detailed steps are as follows:
① Initialization parameters including initial coordinates, maximum number of steps Nm, electrostatic field database, step size, safe distance, etc. Wherein the initial coordinates refer to the position of a river well that is generally randomly selected or designated; the maximum number of steps Nm refers to the maximum limit value of a single trip, and is generally set to 100; the electrostatic field database is the electrostatic field-like map obtained in the step S2; the step length is generally set to 25-50; the safe distance refers to the distance limit value of the walking path from the non-river well, and in order to prevent the path from touching the non-river well, an alarm is triggered below the limit value, generally set to 25, and the safety distance can be adjusted according to the width of the river, and the parameters are used for controlling the random walking behavior and conditions.
② The auxiliary function probCompute is loaded, and the main function of the function is to calculate walking probabilities for each direction in the walking simulation, typically 1 right, 2 left, 3 up, 4 down, 5 down, 6 down, 7 up left, 8 up right. It calculates the gradient of the electric field in each direction at the current position by a specified calculation radius, generally set at 50, and converts these gradients into probabilities.
An auxiliary function probCompute whose mathematical model is as follows:
gradient calculation:
gi = (Vi - V0 ) / r0；
Where g i represents the i-direction electrostatic field intensity gradient, V i is the i-direction electrostatic field intensity, V 0 is the current position electrostatic field intensity, and r 0 is the calculated radius specified in step ②.
Gradient value deformation:
qi = exp(gi)；
g i is used in the exponential function to derive i-direction raw probability q i, which can amplify the effect of gradient differences.
Adjusting probability weights:
；
Wherein c i is the weight coefficient of the i direction, which is 1 by default, and the proportion of each direction can be adjusted according to subjective experience; c j is the weight coefficient in the j direction; q j is the j-direction original probability ;pi is the i-direction final adjusted probability;
The formulas together form a mathematical foundation of probCompute functions, so that the strength of the electrostatic field can be converted into probability, and further, the direction selection of random walk is guided;
③ Setting the overall direction of default walking: if the overall direction is not explicitly specified, the default setting is to walk southward;
④ Initializing coordinate variables: setting an initial coordinate (x 0, y 0) and a current coordinate (x_temp, y_temp) for iteration;
⑤ Walking cycle: a while loop is set. The number of steps in the loop will start from 1, and the number of steps in the iteration will be increased by 1 until the number of steps reaches the set maximum number of steps Nm. If the trigger termination condition (such as out of bounds, touching an obstacle, etc.) is triggered during the period, the while cycle is stopped in advance;
⑥ And (3) detecting a cycle termination condition: in each iteration of the while loop described above, it is checked whether the current coordinates are outside of the scope of investigation, hit a non-river well (i.e., the non-river well protection mechanism in FIG. 2), or have been previously walked past (i.e., the repeat avoidance mechanism in FIG. 2). If one of these conditions is met, a termination condition is triggered and the while loop is stopped;
⑦ Calculating the following direction: in each iteration of the while loop, calculating probability distribution of the next walking direction by using probCompute functions based on the current coordinates, an electrostatic field database and other parameters, and then selecting one direction to walk according to the probability distribution;
⑧ Updating coordinates: updating the current coordinates according to the direction selected in step ⑦, and then adding the new current coordinates to the path list;
⑨ Outputting a result: when the while loop is over or stopped, the biased random walk subroutine returns a data table containing all the x and y coordinates of the steps, i.e., the program walk results.
The invention has the beneficial effects that:
The partial random walk model can conveniently and rapidly reveal the spread of the river channel sand bodies in each period, effectively improves the oil reservoir description efficiency, and can well accord with the field geological experience.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a general technical roadmap for a biased random walk algorithm;
FIG. 2 is a schematic diagram of a quasi-electrostatic field biased random walk subroutine technique;
FIG. 3 is a diagram illustrating an example of well pattern distribution of river points and non-river points according to an embodiment of the present invention;
FIG. 4 is a map of electrostatic field-like fields according to an embodiment of the present invention;
FIG. 5 is a diagram of a walk trajectory utilizing an embodiment of the present invention;
FIG. 6 is a main streamline distribution diagram of a river channel according to an embodiment of the present invention;
FIG. 7 is a diagram of a simulated river channel utilizing an embodiment of the present invention;
FIG. 8 is a hand-drawn riverway control diagram according to an embodiment of the invention.
Detailed Description
The following takes an example of a two-section split river development stratum of an east camping concave S block, and the embodiment of the invention is fully described with reference to the accompanying drawings, and obviously, the described examples are a part of examples, but not all examples of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The specific implementation steps are as follows:
Step S1, establishing a well position distribution map
① And selecting a stratum unit with a certain small level, determining whether each well in the stratum unit has river channel sand bodies through analysis of logging data such as a natural potential curve, a gamma curve and the like, recording the top surface elevation of the sand bodies if the river channel sand bodies exist, and simultaneously calculating the interfacial distance between the elevation and the top of the stratum unit.
② Subdividing stratum units into different periods according to a certain distance tolerance range (3 m), selecting one period, marking a river channel according to whether each well develops a river channel sand body, otherwise marking a non-river channel, and establishing a well position distribution diagram of river points and non-river points according to the period.
As shown in FIG. 3, the study area was located in a square area about 3000 meters in length and width, and there were a total of 251 wells, of which there were 165 non-river wells and 86 total river wells, and the two types of wells were distributed relatively uniformly in the study area.
Step S2, establishing a quasi-electrostatic field map
① Simulating an electrostatic field, wherein each well point is used as a charge source point, the river well points are used as positive charge source points, and the non-river well points are used as negative charge source points;
For electrostatic source points, the electric field strength is very large near the source point and decreases exponentially with increasing distance. The specific calculation formula is as follows:
；
Wherein I is the electric field strength at a given location; i 0 is the intensity of the electrostatic source point; d is the distance from a given point to the electrostatic source point; ls is the characteristic length.
② And (5) superposing the electric field intensity of each well, and establishing an electrostatic field-like map.
As shown in fig. 4, the river wells and the non-river wells serve as a positive power supply point and a negative power supply point respectively, and are similar to a real electrostatic field, the electric field intensity also has the capability of superposition, and for all well positions, the superposition forms an electrostatic field map.
Step S3, random walk simulation
The electrostatic field map based on river channel wells and non-river channel wells is similar to the principle of like-pole repulsion and opposite-pole attraction, and in the like-electrostatic field, walked seeds are negatively charged, so that the seeds walk to river channel well points more easily, and do not walk to non-river channel well points easily. The method comprises the following specific steps:
① Initializing parameters, and setting the maximum step number Nm to be 100; the step size is set to 30; the safe distance is set to 25;
② Loading an auxiliary function probCompute, setting the calculation radius as 50, calculating the electric field gradients of the current position in all directions, and converting the gradients into probabilities;
③ Setting the walking direction as walking in the south direction;
④ Initializing coordinate variables: setting an initial coordinate (x 0, y 0), and setting the initial value of the current coordinate (x_temp, y_temp) to be the same as (x 0, y 0);
⑤ Walking cycle: a while loop is set. The number of the circulating steps starts from 1, and the number of the iterative steps is increased by 1 until the number of the steps reaches 100. If the termination condition is triggered during the period, the while cycle is stopped in advance;
⑥ And (3) detecting a cycle termination condition: at each iteration of the while loop, it is checked whether the current coordinates are outside of the scope of investigation, hit non-river wells, hit points that have been walked before. If one of these conditions is met, a termination condition is triggered and the while loop is stopped;
⑦ Calculating the following direction: in each iteration of the while loop, calculating probability distribution of the next walking direction by using probCompute functions based on the current coordinates, an electrostatic field database and other parameters, and then selecting one direction to walk according to the probability distribution;
⑧ Updating coordinates: updating the current coordinates according to the direction selected in step ⑦, and then adding the new current coordinates to the path list;
⑨ Outputting a result: when the while loop is over or interrupted, the biased random walk subroutine returns a data table containing all the x and y coordinates of the steps, i.e., the program walk results.
In fig. 5, the travelling track estimation using the a well as the starting point is traversing the north-south of the study area, and the track points are connected with the river wells in the overall direction, thereby bypassing the non-river wells.
S4, establishing a river channel main streamline distribution map
And (3) traversing all river wells according to the method of the step (S3) to obtain the river main streamline distribution diagram 6 of the embodiment. In fig. 6, all river well points are taken as starting points, and a repeated avoidance mechanism is matched, so that a series of migration tracks are obtained according to the method of the step S3, and the migration tracks are combined together to form a main streamline distribution diagram of the river.
S5, establishing a river channel distribution diagram
On the basis of the main streamline distribution diagram, the trajectory is subjected to smoothing treatment, and the main streamline is widened to 100m, so that a more real river channel shape is obtained. As shown in fig. 7, the river channels diverge and meet at different positions, the river channels cover all river channel well points, and meanwhile, all non-river channel well points are avoided, and the overall direction of the river channel is in the north-south direction.
Fig. 8 is a hand-drawn river map obtained by manually drawing by geologist under the same data condition, and it can be seen that the simulated river map has higher similarity with the hand-drawn river map, reflecting higher reliability of the method.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
Claims (3)
1. The method for modeling the reservoir of the diversion river channel system based on the partial random walk is characterized by comprising the following steps of:
S1, selecting a stratum of a certain period, determining whether the river channel type attribute of each well is a river channel well or a non-river channel well by a logging identification method, and establishing a well position distribution diagram; river wells refer to wells drilled into a river for a period of secondary formation, while non-river wells refer to wells not drilled into a river for a period of secondary formation;
s2, simulating an electrostatic field idea, wherein each well point is used as a charge source point, a river channel point is used as a positive charge source point, a non-river channel point is used as a negative charge source point, and the electric field intensity is attenuated and overlapped along with the distance, so that an electrostatic field-like map is established;
S3, starting from a selected river channel well, carrying out biased random walk in an electrostatic field-like map according to attraction or repulsion strength through a biased random walk subroutine, and respectively walking in opposite directions, wherein the walk tracks are connected to obtain a river channel main streamline track, and carrying out random walk simulation in the electrostatic field-like map; the partial random walk subroutine is used for simulating random walk in the virtual environment driven by the quasi-electrostatic field map data, and comprises the following detailed steps:
① Initializing parameters: the method comprises the steps of including initial coordinates, namely, randomly selecting or designating the position of a river well in general; maximum step number Nm, namely the maximum limit value of single trip; an electrostatic field database, namely an electrostatic field-like map obtained in the step S2; step length, generally setting the range to 25-50; the safe distance refers to the distance limit value of the walking path from the non-river well, and when the distance is lower than the limit value, an alarm is triggered, generally set to 25, or the distance is adjusted according to the width of the river, and the parameters are used for controlling the behavior and the condition of random walking;
② Loading auxiliary functions probCompute: the main function of the function is to calculate walking probabilities in each direction in walking simulation, generally according to 1, 2,3, 4, 5, 6, 7, 8, and the function is to calculate the gradient of the electric field in each direction at the current position by a specified calculation radius, generally set to 50, and convert the gradients into probabilities;
an auxiliary function probCompute whose mathematical model is as follows:
gradient calculation:
；
Where g i represents the i-direction electrostatic field intensity gradient, V i is the i-direction electrostatic field intensity, V 0 is the current position electrostatic field intensity, r 0 is the specified calculated radius in step ②;
Gradient value deformation:
qi = exp(gi)；
g i is used in an exponential function to obtain the i-direction original probability q i, and the influence of gradient difference is amplified;
adjusting probability weights:
；
Wherein c i is the weight coefficient of the i direction, which is 1 by default, and the proportion of each direction can be adjusted according to subjective experience; c j is the weight coefficient in the j direction; q j is the original probability in the j direction; p i is the probability of the i direction being finally adjusted;
The formulas together form a mathematical foundation of probCompute functions, so that the strength of the electrostatic field can be converted into probability, and further, the direction selection of random walk is guided;
③ Setting the overall direction of default walking: if the overall direction is not explicitly specified, the default setting is to walk southward;
④ Initializing coordinate variables: setting an initial coordinate (x 0, y 0) and a current coordinate (x_temp, y_temp) for iteration;
⑤ Walking cycle: setting a while loop, wherein the number of steps of the loop starts from 1, and iterating one step to add 1 until the number of steps reaches the set maximum number Nm, and stopping the while loop in advance if a termination condition is triggered;
⑥ And (3) detecting a cycle termination condition: in each iteration of the while loop, checking whether the current coordinates are outside of the investigation range, touch non-river wells, or points that have been walked past before, if one of these conditions is met, a termination condition is triggered, and the while loop is stopped;
⑦ Calculating the following direction: in each iteration of the while loop, calculating probability distribution of the next walking direction by using probCompute functions based on the current coordinates, an electrostatic field database and other parameters, and then selecting one direction to walk according to the probability distribution;
⑧ Updating coordinates: updating the current coordinates according to the direction selected in step ⑦, and then adding the new current coordinates to the path list;
⑨ Outputting a result: when the while cycle is ended or stopped, the biased random walk subroutine returns a data table containing all the x and y coordinates of steps, i.e., the result of the program walk;
S4, traversing all river wells according to the method of S3 to obtain a river main streamline distribution diagram;
s5, changing the main streamline of the river channel into a more real river channel form based on the main streamline distribution map on the basis of the river channel configuration parameters, and obtaining the river channel distribution map.
2. The method for modeling the reservoir of the diversion river system based on the partial random walk according to claim 1, wherein the specific method in the step S1 is that a stratum unit with a certain small level is selected, whether river sand exists in each well in the stratum unit is determined through well logging data analysis, if so, the top surface elevation of the sand is recorded, and meanwhile, the interface distance between the elevation and the top of the stratum unit is calculated; subdividing stratum units into different periods, selecting one period, marking as a river channel well according to whether each well develops a river channel sand body or not, otherwise marking as a non-river channel well, and establishing a well position distribution diagram according to the period.
3. The method for modeling a split-flow river system reservoir based on a partial random walk according to claim 1, wherein the specific steps of step S2 are as follows:
① Simulating an electrostatic field, wherein each well point is used as a charge source point, the river well points are used as positive charge source points, and the non-river well points are used as negative charge source points;
For an electrostatic source point, the electric field strength is very large near the source point and decreases exponentially with increasing distance, and the specific calculation formula is as follows:
；
wherein I is the electric field strength at a given location; i 0 is the intensity of the electrostatic source point; d is the distance from a given point to the electrostatic source point; ls is the characteristic length;
② And (5) superposing the electric field intensity of each well, and establishing an electrostatic field-like map.
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| US6549879B1 (en) * | 1999-09-21 | 2003-04-15 | Mobil Oil Corporation | Determining optimal well locations from a 3D reservoir model |
| CN110874505A (en) * | 2018-08-14 | 2020-03-10 | 中国石油天然气股份有限公司 | Braided river reservoir modeling method and device |
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