Patent Application
20240328313
The present disclosure relates generally to gas carbonate reservoir characterization and development.
Carbonate reservoirs are much more complex than clastic reservoirs (consolidated sedimentary reservoirs). For example, porosity and permeability variations in carbonate reservoirs are far more drastic than in clastic reservoirs. Therefore, within carbonate reservoirs, the bulk of the gas production comes from discrete thin zones within the reservoir. The challenge is how to define these zones with the most potential to produce gas so that perforation operations can be targeted to the right zones.
Further, knowledge of potential production levels gleaned from clastic reservoirs is not always applicable to carbonate reservoirs because that analysis of clastic reservoirs often relies heavily on analyses of core plugs.
Resistivity is one measurement that has been used in various reservoirs to estimate porosity and water saturation of zones within the reservoir. Resistivity logs, electrical well logs that record the resistivity of a formation, can be performed for shallow, medium, or deep penetration into the formation around the wellbore. Correlations between resistivity and porosity or water saturation have been well studied for clastic reservoirs. However, in carbonate reservoirs, other factors like the tighter porosity can contribute to resistivity values in ways that, if clastic teachings alone are applied, lead to false-positives when identifying potentially gas-bearing zones. Attempts to overcome this issue have included manipulating the resistivity data.
For example, one study multiplies the resistivity data by twice of the percent porosity, which is believed to assist in differentiating coarse, porous dolomite from tight dolomite. Another method first takes the difference between the resistivity before and after flushing and then multiplies the difference by twice of the percent porosity. In each of these examples, the raw resistivity data was used.
Various details of the present disclosure are hereinafter summarized to provide a basic understanding. This summary is not an exhaustive overview of the disclosure and is intended neither to identify certain elements of the disclosure, nor to delineate the scope thereof. Rather, the primary purpose of this summary is to present some concepts of the disclosure in a simplified form prior to the more detailed description that is presented hereinafter.
A method of the present disclosure may comprise: conditioning deep resistivity data for a plurality of zones of a subterranean formation, thereby producing conditioned deep resistivity data; integrating the conditioned deep resistivity data with effective porosity data for the plurality of zones, thereby producing a flow index curve for each of the plurality of zones; and identifying one or more potential gas-producing zones from the plurality of zones based on the flow index curve.
A method of the present disclosure may comprise: conditioning deep resistivity data for a plurality of zones of a subterranean formation, thereby producing conditioned deep resistivity data, wherein the conditioning involves producing a cross-plot of the deep resistivity data and the effective porosity data, wherein data points in a low effective porosity, high deep resistivity region of the cross-plot are conditioned; integrating the conditioned deep resistivity data with effective porosity data for the plurality of zones, thereby producing a flow index curve for each of the plurality of zones, wherein the integrating comprises (a) value=(conditioned deep resistivity)*(effective porosity)n where 1<n≤10 and optionally (b) normalizing the values, wherein the flow index curve is either based on the values or the normalized values; identifying one or more potential gas-producing zones from the plurality of zones based on the flow index curve; and performing a wellbore operation on at least one of the one or more potential gas-producing zones.
A machine-readable storage medium of the present disclosure having stored thereon a computer program for identifying one or more potential gas-producing zones, the computer program comprising a routine of set instructions for causing the machine to perform the steps of: conditioning deep resistivity data for a plurality of zones of a subterranean formation, thereby producing conditioned deep resistivity data; integrating the conditioned deep resistivity data with effective porosity data for the plurality of zones, thereby producing a flow index curve for each of the plurality of zones; and identifying the one or more potential gas-producing zones from the plurality of zones based on the flow index curve.
Any combinations of the various embodiments and implementations disclosed herein can be used in a further embodiment, consistent with the disclosure. These and other aspects and features can be appreciated from the following description of certain embodiments presented herein in accordance with the disclosure and the accompanying drawings and claims.
Embodiments of the present disclosure will now be described in detail with reference to the accompanying Figures. Like elements in the various figures may be denoted by like reference numerals for consistency. Further, in the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the claimed subject matter. However, it will be apparent to one of ordinary skill in the art that the embodiments disclosed herein may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description. Additionally, it will be apparent to one of ordinary skill in the art that the scale of the elements presented in the accompanying Figures may vary without departing from the scope of the present disclosure.
Embodiments in accordance with the present disclosure generally relate to the characterization and development of gas carbonate reservoirs. More specifically, the present disclosure aims to identify zones within a carbonate reservoir with (a) high porosity and high resistivity or (b) low porosity and high resistivity as these are potential gas-producing zones. Identifying such zones uses both resistivity data and effective porosity data. Unlike previous methods, the methods of the present disclosure condition the resistivity data before integration with the effective porosity data. Without being limited by theory, it is believed that by conditioning the resistivity data before integration with the effective porosity data reduces the contribution of a tight zone with very high resistivity being inaccurately identified as a potential gas-producing zone.
In a first example, the flow index curve 112 itself may be used for identifying 116 zones within the formation that have the potential to produce gas. For example, a zone along the flow index curve 112 with a high flow index (e.g., greater than 0.5) may be identified 116 as a potential gas-producing zone 114a. Accordingly, a stimulation (e.g., perforation and fracturing) operation may be preferentially performed on the potential gas-producing zone 114a over other zones with a lower flow index.
In a second example method, the flow index values from the flow index curve 112 may be used as an input an input to a model 118 of the formation (e.g., a 2-dimensional mapping, a 3-dimensional model, or the like), where the model 118 uses the flow index values as at least one input for identifying 120 a potential gas-producing zone 114b. Accordingly, a stimulation (e.g., perforation and fracturing) operation may be preferentially performed on the potential gas-producing zone 114b over other zones with a lower flow index.
In yet another example method, the flow index curve 112 may be used for classifying 122 zones within the formation using a scale related to potential gas production, and the values 124 (or classifications) of the scale assigned to particular zones (e.g., specific depths or over a range of depths) within the formation may be used in a model 126 of the formation as at least one of the inputs for identifying 128 a potential gas-producing zone 114c. Accordingly, a stimulation (e.g., perforation and fracturing) operation may be preferentially performed on the potential gas-producing zone 114c over other zones with a lower flow index.
The resistivity data used in the methods of the present disclosure is preferably deep resistivity data may be gathered by wireline or other logging methods. By using deep resistivity data, the resistivity measurements more accurately represent the undisturbed and natural formation. In contrast, shallow and medium resistivity data may be effected by fluid infiltration from the wellbore.
Conditioning of the deep resistivity data can be performed using a cross-plot of effective porosity and deep resistivity data.
The illustrated example conditions the data by simply changing the deep resistivity data within a set region to a set value. Alternatively, a mathematical function may be applied to the deep resistivity data within a set region. For example, the deep resistivity data may be divided by a number of 2 to 10 with the caveat that a resistivity threshold is not to be crossed. That is, if the minimum deep resistivity threshold is 150 Ohm·m (so nothing can be conditioned to lower than 150 Ohm·m) and the conditioning formula is divide the deep resistivity value by 10, then a first data point falling within the region to be conditioned and having a resistivity of 1000 Ohm·m would be conditioned to 150 Ohm·m, and a second data point falling within the region to be conditioned and having a resistivity of 2000 Ohm·m would be conditioned to 200 Ohm·m.
The low effective porosity, high deep resistivity region that defines the data points to be conditioned may be set threshold values, for example, as illustrated in
One skilled in the art will recognize other mathematical methods for defining the low effective porosity, high deep resistivity region used to identify the data points in the cross-plot that undergo conditioning of the corresponding deep resistivity data.
After the deep resistivity data is conditioned, it can be integrated with the effective porosity data. Integration may involve one or more mathematical operations that incorporate both the conditioned deep resistivity data and the effective porosity data. An example of integration is multiplying the conditioned deep resistivity at each depth by twice the effective porosity at the same depth (flow index=(conditioned deep resistivity)*(effective porosity)2), which produces a flow index curve. Other mathematical formulas may be used for integrating the conditioned deep resistivity data with the effective porosity data. For example, flow index=(conditioned deep resistivity)*(effective porosity)n where 1<n≤10 (or 1.5≤n≤10, or 1.7≤n≤6, or 2≤n≤4).
In another example, producing a flow index curve may be a two-step process with a first step being one or more mathematical operation that involves both the conditioned deep resistivity data and the effective porosity data and a second step being normalizing the resultant values to produce a flow index curve (or a normalized flow index curve). For example, a two-step integration may include first integration step according to flow index=(conditioned deep resistivity)*(effective porosity)2 or another suitable mathematical function and a second step according to FInorm=(FI−FImin)/(FImax−FImin) where FInorm is the normalized flow index value for the corresponding flow index value (FI), FImax is the maximum flow index value produced in the first step, and FImin is the minimum flow index value produced in the first step. The resultant FInorm is a unitless value.
By using the conditioned deep resistivity data (sixth track), the seventh track has better defined and more readily identifiable peaks as compared to the fifth track that uses the raw deep resistivity data (third track). The flow index curve or individual flow index values therein may be used for classifying and/or identifying potentially gas-producing zones. Generally, higher flow index values indicate higher gas flow.
The flow index values may be used to classify the zones within the formation. In the example illustrated in
As an indication of the viability of the methods described herein, the resistivity and effective porosity data for three wells of known production level were re-analyzed according to the methods described herein.
In the upper example, a vertical mother wellbore was drilled with four UBCTD lateral wellbores extending therefrom. No gas flow was observed. The deep resistivity data and effective porosity were re-analyzed according to the methods described herein and illustrate in the flow index curve (right track) that no gas production should have been expected using UBCTD methods.
In the middle example, a vertical mother wellbore was drilled with three UBCTD lateral wellbores extending therefrom. High gas production rates were observed, which is consistent with the flow index curve having zones in the FT1 classification per Table 1.
In the lower example, a vertical mother wellbore was drilled with three UBCTD lateral wellbores extending therefrom. Very high gas production rates were observed, which is consistent with the flow index curve larger zones in the FT1 classification per Table 1.
The foregoing validation was performed for dozens of wells where the classification of Table 1 held true.
The methods of the present disclosure are useful for selecting drilling and stimulation operations for new wells and existing wells. For existing wells, the existing deep resistivity data and effective porosity data for a formation can be re-analyzed and/or new data can be gathered and analyzed by the methods described herein to identify potential gas-producing zones. Based on the classification of zones within the formation, new wells may be placed to access potential gas-producing zones, new UBCTD may be undertaken to access potential gas-producing zones, and new fracturing operations may be undertaken to increase gas production in the identified zones.
As discussed above, the flow index curve, values from the flow index curve, and/or classifications based on the flow index curve may be used as inputs for a model of the subterranean formation. The model may be a 2-dimensional or a 3-dimensional model of the formation. The model may be used for identifying new well locations, identifying zones for well placement, and/or identifying zones for stimulation operations.
In view of the structural and functional features described above, example methods will be better appreciated with reference to
In view of the foregoing structural and functional description, those skilled in the art will appreciate that portions of the embodiments may be embodied as a method, data processing system, or computer program product. Accordingly, these portions of the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware, such as shown and described with respect to the computer system of
Certain embodiments have also been described herein with reference to block illustrations of methods, systems, and computer program products. It will be understood that blocks and/or combinations of blocks in the illustrations, as well as methods or steps or acts or processes described herein, can be implemented by a computer program comprising a routine of set instructions stored in a machine-readable storage medium as described herein. These instructions may be provided to one or more processors of a general purpose computer, special purpose computer, or other programmable data processing apparatus (or a combination of devices and circuits) to produce a machine, such that the instructions of the machine, when executed by the processor, implement the functions specified in the block or blocks, or in the acts, steps, methods and processes described herein.
These processor-executable instructions may also be stored in computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture including instructions which implement the function specified. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.
In this regard,
Computer system 600 includes processing unit 602, system memory 604, and system bus 606 that couples various system components, including the system memory 604, to processing unit 602. Dual microprocessors and other multi-processor architectures also can be used as processing unit 602. System bus 606 may be any of several types of bus structure including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. System memory 604 includes read only memory (ROM) 610 and random access memory (RAM) 612. A basic input/output system (BIOS) 614 can reside in ROM 610 containing the basic routines that help to transfer information among elements within computer system 600.
Computer system 600 can include a hard disk drive 616, magnetic disk drive 618, e.g., to read from or write to removable disk 620, and an optical disk drive 622, e.g., for reading CD-ROM disk 624 or to read from or write to other optical media. Hard disk drive 616, magnetic disk drive 618, and optical disk drive 622 are connected to system bus 606 by a hard disk drive interface 626, a magnetic disk drive interface 628, and an optical drive interface 630, respectively. The drives and associated computer-readable media provide nonvolatile storage of data, data structures, and computer-executable instructions for computer system 600. Although the description of computer-readable media above refers to a hard disk, a removable magnetic disk and a CD, other types of media that are readable by a computer, such as magnetic cassettes, flash memory cards, digital video disks and the like, in a variety of forms, may also be used in the operating environment; further, any such media may contain computer-executable instructions for implementing one or more parts of embodiments shown and described herein.
A number of program modules may be stored in drives and RAM 610, including operating system 632, one or more application programs 634, other program modules 636, and program data 638. In some examples, the application programs 634 and program data 638 can include functions and methods programmed to condition the deep resistivity data, integrate the conditioned deep resistivity data and the effective porosity data, and identify and/or classify zones based on the flow index curve, such as shown and described herein.
A user may enter commands and information into computer system 600 through one or more input devices 640, such as a pointing device (e.g., a mouse, touch screen), keyboard, microphone, joystick, game pad, scanner, and the like. For instance, the user can employ input device 640 to edit or modify the parameters for conditioning the deep resistivity data, integrating the conditioned deep resistivity data and the effective porosity data, and identifying and/or classifying zones based on the flow index curve. These and other input devices 640 are often connected to processing unit 602 through a corresponding port interface 642 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, serial port, or universal serial bus (USB). One or more output devices 644 (e.g., display, a monitor, printer, projector, or other type of displaying device) is also connected to system bus 606 via interface 646, such as a video adapter.
Computer system 600 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer 648. Remote computer 648 may be a workstation, computer system, router, peer device, or other common network node, and typically includes many or all the elements described relative to computer system 600. The logical connections, schematically indicated at 650, can include a local area network (LAN) and/or a wide area network (WAN), or a combination of these, and can be in a cloud-type architecture, for example configured as private clouds, public clouds, hybrid clouds, and multi-clouds. When used in a LAN networking environment, computer system 600 can be connected to the local network through a network interface or adapter 652. When used in a WAN networking environment, computer system 600 can include a modem, or can be connected to a communications server on the LAN. The modem, which may be internal or external, can be connected to system bus 606 via an appropriate port interface. In a networked environment, application programs 634 or program data 638 depicted relative to computer system 600, or portions thereof, may be stored in a remote memory storage device 654.
Embodiment 1. A method comprising: conditioning deep resistivity data for a plurality of zones of a subterranean formation, thereby producing conditioned deep resistivity data; integrating the conditioned deep resistivity data with effective porosity data for the plurality of zones, thereby producing a flow index curve for each of the plurality of zones; and identifying one or more potential gas-producing zones from the plurality of zones based on the flow index curve.
Embodiment 2. The method of Embodiment 1, wherein the conditioning of the deep resistivity data comprises: producing a cross-plot of the deep resistivity data and the effective porosity data, wherein data points in a low effective porosity, high deep resistivity region of the cross-plot are conditioned.
Embodiment 3. The method of any one of Embodiments 1-2, wherein the integrating of the conditioned deep resistivity data with the effective porosity data comprises: (conditioned deep resistivity)*(effective porosity)n where 1<n≤10.
Embodiment 4. The method of any one of Embodiments 1-2, wherein the integrating of the conditioned deep resistivity data with the effective porosity data comprises: value=(conditioned deep resistivity)*(effective porosity)n where 1<n≤10 and the flow index curve is the values normalized.
Embodiment 5. The method of any one of Embodiments 1-4, wherein the identifying the one or more potential gas-producing zones comprises:
classifying each of the plurality of zones by a flow type based on the flow index curve.
Embodiment 6. The method of any one of Embodiments 1-5, wherein the identifying the one or more potential gas-producing zones comprises: classifying each of the plurality of zones by a flow type based on the flow index curve; and applying the flow type of the plurality of zones as an input to a model of the subterranean formation.
Embodiment 7. The method of any one of Embodiments 1-6, wherein the identifying the one or more potential gas-producing zones comprises: applying the flow index curve for each of the plurality of zones as an input to a model of the subterranean formation.
Embodiment 8. The method of any one of Embodiments 1-7, wherein the integrating of the conditioned deep resistivity data with the effective porosity data comprises a normalizing step such that values in the flow index curve range from 0 to 1, and wherein the identifying of the one or more potential gas-producing zones comprises: classifying the plurality of zones based as Flow Type 1 with a flow index of greater than or equal to 0.5, Flow Type 2 with the flow index between 0.2 and 0.5, and Flow Type 3 with the flow index less than or equal to 0.2.
Embodiment 9. The method of Embodiment 8 further comprising: performing an under balanced coil tube drilling operation on at least one of the plurality of zones classified as the Flow Type 1.
Embodiment 10. The method of any one of Embodiments 8-9 further comprising: performing a stimulation operation on at least one of the plurality of zones classified as the Flow Type 2.
Embodiment 11. The method of any one of Embodiments 8-10 further comprising: not performing a stimulation operation on at least one of the plurality of zones classified as the Flow Type 3.
Embodiment 12. A method comprising: conditioning deep resistivity data for a plurality of zones of a subterranean formation, thereby producing conditioned deep resistivity data, wherein the conditioning involves producing a cross-plot of the deep resistivity data and the effective porosity data, wherein data points in a low effective porosity, high deep resistivity region of the cross-plot are conditioned; integrating the conditioned deep resistivity data with effective porosity data for the plurality of zones, thereby producing a flow index curve for each of the plurality of zones, wherein the integrating comprises (a) value=(conditioned deep resistivity)*(effective porosity)n where 1<n≤10 and optionally (b) normalizing the values, wherein the flow index curve is either based on the values or the normalized values; identifying one or more potential gas-producing zones from the plurality of zones based on the flow index curve; and performing a wellbore operation on at least one of the one or more potential gas-producing zones.
Embodiment 13. A machine-readable storage medium having stored thereon a computer program for identifying one or more potential gas-producing zones, the computer program comprising a routine of set instructions for causing the machine to perform the steps of: conditioning deep resistivity data for a plurality of zones of a subterranean formation, thereby producing conditioned deep resistivity data; integrating the conditioned deep resistivity data with effective porosity data for the plurality of zones, thereby producing a flow index curve for each of the plurality of zones; and identifying the one or more potential gas-producing zones from the plurality of zones based on the flow index curve.
Embodiment 14. The machine-readable storage medium of Embodiment 13, wherein the conditioning of the deep resistivity data comprises: producing a cross-plot of the deep resistivity data and the effective porosity data, wherein data points in a low effective porosity, high deep resistivity region of the cross-plot are conditioned.
Embodiment 15. The machine-readable storage medium of any one of Embodiments 13-14, wherein the integrating of the conditioned deep resistivity data with the effective porosity data comprises: (conditioned deep resistivity)*(effective porosity)n where 1<n≤10.
Embodiment 16. The machine-readable storage medium of any one of Embodiments 13-15, wherein the identifying the one or more potential gas-producing zones comprises: classifying each of the plurality of zones by a flow type based on the flow index curve.
Embodiment 17. The machine-readable storage medium of any one of Embodiments 13-16, wherein the identifying the one or more potential gas-producing zones comprises: classifying each of the plurality of zones by a flow type based on the flow index curve; and applying the flow type of the plurality of zones as an input to a model of the subterranean formation.
Embodiment 18. The machine-readable storage medium of any one of Embodiments 13-17, wherein the identifying the one or more potential gas-producing zones comprises: applying the flow index curve for each of the plurality of zones as an input to a model of the subterranean formation.
Embodiment 19. The machine-readable storage medium of any one of Embodiments 13-18, wherein the integrating of the conditioned deep resistivity data with the effective porosity data comprises a normalizing step such that values in the flow index curve range from 0 to 1, and wherein the identifying of the one or more potential gas-producing zones comprises: classifying the plurality of zones based as Flow Type 1 with a flow index of greater than or equal to 0.5, Flow Type 2 with the flow index between 0.2 and 0.5, and Flow Type 3 with the flow index less than or equal to 0.2.
Embodiment 20. The machine-readable storage medium of Embodiment 19, wherein the set of instructions further causing the machine to provide a recommendation regarding: (a) performing an under balanced coil tube drilling operation on at least one of the plurality of zones classified as the Flow Type 1; (b) performing a stimulation operation on at least one of the plurality of zones classified as the Flow Type 2; (c) not performing a stimulation operation on at least one of the plurality of zones classified as the Flow Type 3; or (d) any combination of two or more of (a), (b), and (c).
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, for example, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “contains”, “containing”, “includes”, “including,” “comprises”, and/or “comprising,” and variations thereof, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Terms of orientation used herein are merely for purposes of convention and referencing and are not to be construed as limiting. However, it is recognized these terms could be used with reference to an operator or user. Accordingly, no limitations are implied or to be inferred. In addition, the use of ordinal numbers (e.g., first, second, third, etc.) is for distinction and not counting. For example, the use of “third” does not imply there must be a corresponding “first” or “second.” Also, if used herein, the terms “coupled” or “coupled to” or “connected” or “connected to” or “attached” or “attached to” may indicate establishing either a direct or indirect connection, and is not limited to either unless expressly referenced as such.
While the disclosure has described several exemplary embodiments, it will be understood by those skilled in the art that various changes can be made, and equivalents can be substituted for elements thereof, without departing from the spirit and scope of the invention. In addition, many modifications will be appreciated by those skilled in the art to adapt a particular instrument, situation, or material to embodiments of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, or to the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, or component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative.
