Drilling temperature and oil and gas reservoir forming period prediction method and drilling temperature prediction device
By collecting and analyzing rock thermal conductivity data during oil and gas exploration, a thermal conductivity model was established, which solved the problem of deep temperature prediction bias and achieved more accurate prediction of temperature and hydrocarbon accumulation stages.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2022-06-02
- Publication Date
- 2026-07-24
AI Technical Summary
In actual exploration and production, the lack of deep thermal conductivity data makes it easy to overestimate the geothermal gradient of deep formations when analyzing the temperature of a single well. Furthermore, the lack of consideration of formation depth when studying thermal conductivity leads to deviations in temperature prediction.
By collecting field or well samples in the study area, we measured the thermal conductivity data of deep carbonate rock formations. Combined with the thermal conductivity data of clastic rocks and rocks under different porosity conditions, we established a rock thermal conductivity model. We used a one-dimensional heat conduction equation to simulate and calculate the formation temperature, dynamically analyzed the change of rock thermal conductivity with depth, and made more accurate temperature predictions.
It has enabled more accurate prediction of deep formation temperature characteristics and determination of single-well hydrocarbon accumulation stages, providing a more reliable basis for reconstructing burial history.
Smart Images
Figure CN117216924B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas exploration technology, specifically to a method for predicting drilling temperature and oil and gas accumulation stages, and a device for predicting drilling temperature. Background Technology
[0002] The geothermal gradient is one of the most fundamental geothermal parameters, commonly used in oil and gas basins to describe the vertical variation of drilling temperature. In basin analysis, temperature is a crucial factor to consider in determining organic matter thermal evolution, hydrocarbon phases, and the use of drilling instruments. Previous research identifies two main parameters influencing temperature variation: rock thermal conductivity and geothermal heat flow. In actual exploration and production, the lack of deep thermal conductivity data can easily lead to overestimation of the geothermal gradient in deep formations during single-well temperature analysis. Furthermore, the failure to consider formation depth when studying thermal conductivity results in biased temperature predictions. Summary of the Invention
[0003] In view of this, embodiments of the present invention provide a method for predicting drilling temperature and hydrocarbon accumulation stages, and a device for predicting drilling temperature. This solves the problem that in actual exploration and production, due to the lack of deep thermal conductivity data, single-well temperature analysis is prone to overestimation of the geothermal gradient in deep formations, and the formation depth is not considered when studying thermal conductivity, resulting in deviations in temperature prediction.
[0004] An embodiment of the present invention provides a drilling temperature prediction method comprising: determining whether there is drilling data in the shallow clastic rock strata of the study area; if so, obtaining the thermal conductivity data of the clastic rock based on the drilling data; if not, obtaining the thermal conductivity data of the rock under different porosity conditions after the change of burial depth based on the thermal conductivity data of the rock measured by the field outcrops in the study area.
[0005] Field or well samples were collected from the corresponding strata in the study area, and the thermal conductivity data of the deep carbonate rock strata were obtained by measuring the field or well samples.
[0006] A rock thermal conductivity model for the study area was established based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata.
[0007] The formation temperature of the well is obtained based on the rock thermal conductivity model of the study area to predict the well temperature.
[0008] In one embodiment, the step of obtaining the thermal conductivity data of clastic rocks based on drilling data includes: obtaining the initial thermal conductivity data of clastic rocks using a one-dimensional heat conduction equation based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value.
[0009] In one embodiment, after the step of obtaining the initial clastic rock thermal conductivity data based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value using a one-dimensional heat conduction equation, the method further includes: correcting the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself in the literature, and finally determining the rock thermal conductivity data of different clastic rock formations.
[0010] In one embodiment, the step of obtaining rock thermal conductivity data under different porosity conditions with varying burial depth based on rock thermal conductivity data measured from outcrops in the study area includes: selecting rock thermal conductivity data measured from outcrops in the area, considering the influence of stratum porosity on rock thermal conductivity, and obtaining rock thermal conductivity data under different porosity conditions with varying burial depth based on the porosity calculation formula, according to the ideal condition of uniform pore space distribution, and considering the influence of porosity on thermal conductivity.
[0011] In one embodiment, the step of measuring the thermal conductivity data of the deep carbonate rock formation from the field or well samples includes: measuring the thermal conductivity data of the deep carbonate rock formation from the field or well samples using a thermal conductivity measuring instrument.
[0012] In one embodiment, the step of obtaining the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature includes: based on the rock thermal conductivity model of the study area, combined with the current regional geothermal heat flow data, using a one-dimensional heat conduction equation to simulate and calculate the formation temperature of the un-drilled well to predict the well temperature.
[0013] An embodiment of the present invention provides a method for predicting the drilling oil and gas accumulation stages, which uses the drilling temperature prediction method described above to predict the drilling temperature, performs drilling burial history reconstruction based on the drilling temperature, and predicts the drilling oil and gas accumulation stages based on the reconstructed drilling burial history.
[0014] An embodiment of the present invention provides a drilling temperature prediction device, comprising:
[0015] The judgment module is used to determine whether there is drilling data in the shallow clastic rock formations of the study area;
[0016] The calculation module is used to obtain the thermal conductivity data of clastic rocks based on drilling data; and to obtain the thermal conductivity data of rocks under different porosity conditions with varying burial depth based on the thermal conductivity data of rocks measured in the field outcrops of the study area.
[0017] The data acquisition module is used to collect field or well samples from the corresponding strata in the study area.
[0018] The measurement module is used to measure the thermal conductivity data of deep carbonate rock formations from the field or drilling samples.
[0019] The model building module is used to build a rock thermal conductivity model for the study area based on the rock thermal conductivity of the clastic rocks, the rock thermal conductivity data under different porosity conditions after the burial depth changes, and the rock thermal conductivity data of the deep carbonate rock strata.
[0020] The analysis module is used to obtain the formation temperature of the well based on the rock thermal conductivity model of the study area, so as to predict the well temperature.
[0021] An embodiment of the present invention provides an electronic device including a memory and a processor. The memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the drilling temperature prediction method as described above.
[0022] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the drilling temperature prediction method described above.
[0023] This invention provides a method and apparatus for predicting drilling temperature and hydrocarbon accumulation stages. The drilling temperature prediction method uses thermal conductivity data corresponding to each rock formation and dynamically analyzes the thermal conductivity values of the same rock formation at different depths based on the changes in rock activity with depth. This establishes a thermal conductivity model, allowing for more accurate analysis of formation temperature changes. Simultaneously, using this new thermal conductivity model, single-well burial history reconstruction can be performed, enabling more accurate prediction of single-well hydrocarbon accumulation stages. This invention can more accurately predict the temperature characteristics of deep formations and, by utilizing thermal conductivity data, more accurately reconstruct the deep burial history process, providing a basis for determining hydrocarbon accumulation stages. Attached Figure Description
[0024] Figure 1 The diagram shows a flowchart of a drilling temperature prediction method provided in an embodiment of the present invention.
[0025] Figure 2 The figure shown is a current temperature simulation analysis diagram of the Shunbei 5 well provided in an embodiment of the present invention.
[0026] Figure 3 The image shown is a schematic diagram of the simulation results of the temperature-burial history recovery map of Well Shunbei 5 provided by an embodiment of the present invention.
[0027] Figure 4 The diagram shown is a schematic diagram of the drilling temperature prediction device provided in an embodiment of the present invention. Detailed Implementation
[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0029] The geothermal gradient is one of the most fundamental geothermal parameters, commonly used in oil and gas basins to describe the vertical variation of drilling temperature. In basin analysis, temperature is a crucial factor to consider in determining organic matter thermal evolution, hydrocarbon phases, and the use of drilling instruments. Based on previous research, two main parameters currently influence temperature variation: rock thermal conductivity and geothermal heat flow.
[0030] Chinese and foreign scholars have made a great deal of research results in the study of geothermal fields in basins. Since the geothermal flow value of a fixed tectonic unit in a geological history is relatively stable, the main factor determining the vertical distribution of geothermal gradient in a certain tectonic unit at a certain period is the change in rock thermal conductivity.
[0031] Previous experiments and analyses have shown that the thermal conductivity parameters of strata rocks vary greatly due to differences in rock properties and burial depth (different degrees of compaction) in different basins. For example, in the Tarim Basin, the thermal conductivity of Miocene rocks is 1.8 W / mK, the thermal conductivity of Middle Ordovician rocks reaches 3.00 W / mK, while the thermal conductivity of Cambrian strata reaches 3.73 W / mK
[11] . The thermal conductivity of Cambrian rocks is almost twice that of Miocene rocks. Even among strata deposited in the same period, the thermal conductivity of Miocene rocks in the Qaidam Basin is 1.90 W / mK, and the thermal conductivity of rocks in different basins is also different. The selection of rock thermal conductivity is crucial for the prediction of stratum temperature. In single-well simulations, the thermal conductivity parameters of rocks are given according to geological models that use fixed rock types and corresponding fixed thermal conductivity values. In the model (taking Petrolmod as an example), the thermal conductivity of limestone is only about 2.2 W / mK, which is significantly different from the actual measured value of 3.00 W / mK in the Ordovician system of the Tarim Basin. If this model with fixed rock thermal conductivity parameters is used for simulation, it will inevitably lead to a large difference between the actual formation temperature value and the simulation value, thus affecting people's judgment on the thermal evolution of organic matter.
[0032] In actual exploration and production, due to the lack of deep thermal conductivity data, single-well temperature analysis generally uses the average geothermal gradient of shallow formations to estimate the deep geothermal gradient, which easily leads to an overestimation of the deep geothermal gradient. Furthermore, when studying thermal conductivity, a single thermal conductivity is typically assigned to a single rock formation unit, without considering the changes in rock thermal conductivity at the corresponding depth based on compaction, resulting in biased temperature predictions.
[0033] To address the aforementioned problems, this invention utilizes the thermal conductivity data of each rock stratum and dynamically analyzes the thermal conductivity values of the same rock stratum at different depths based on the changes in rock activity with depth. This establishes a rock thermal conductivity model, enabling more accurate analysis of formation temperature variations. Furthermore, this new model allows for the reconstruction of single-well burial histories, leading to more accurate predictions of hydrocarbon accumulation phases. This invention can more accurately predict the temperature characteristics of deep formations and, by utilizing thermal conductivity data, more accurately reconstruct the deep burial history, providing a basis for determining hydrocarbon accumulation phases. Specific implementation methods are described in the following examples.
[0034] Example 1:
[0035] This embodiment provides a drilling temperature prediction method, such as... Figure 1 As shown, the drilling temperature prediction method includes:
[0036] Step 01: Determine whether there is drilling data for the shallow clastic rock strata in the study area; if so, obtain the thermal conductivity data of the clastic rocks based on the drilling data; if not, obtain the thermal conductivity data of the rocks under different porosity conditions after the burial depth changes based on the thermal conductivity data of the rocks measured by the field outcrops in the study area.
[0037] The step of obtaining the thermal conductivity data of clastic rocks based on drilling data includes: obtaining the initial thermal conductivity data of clastic rocks using a one-dimensional heat conduction equation based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value.
[0038] Furthermore, after the step of obtaining the initial clastic rock thermal conductivity data based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value using a one-dimensional heat conduction equation, the method further includes: correcting the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself in the literature, and finally determining the rock thermal conductivity data of different clastic rock formations.
[0039] In summary, for the shallow clastic rock strata in the study area, if there is drilling data nearby, the original static temperature measurement data of the drilling strata can be used, combined with the current regional geothermal heat flow value, and the one-dimensional heat conduction equation can be used to directly calculate the thermal conductivity of the clastic rocks. The abnormal temperature data can be corrected by combining the thermal conductivity data of the clastic rocks themselves in the literature, and finally the thermal conductivity data of different clastic rock strata can be determined.
[0040] For the shallow clastic rock strata in the study area, if there is no drilling data near the study area, the steps for obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the rock thermal conductivity data measured by field outcrops in the study area include: selecting the rock thermal conductivity data measured by field outcrops in the area, while considering the influence of formation porosity on rock thermal conductivity, and obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the porosity calculation formula, according to the ideal condition of uniform pore space distribution, and considering the influence of porosity on thermal conductivity.
[0041] Step 02: Collect field or well samples of the corresponding strata in the study area, and measure the thermal conductivity data of the deep carbonate rock strata by measuring the field or well samples.
[0042] The step of measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples includes: measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples using a thermal conductivity measuring instrument.
[0043] In summary, there is limited data on the thermal conductivity of deep carbonate rock formations in previous studies. It is necessary to collect field or well samples of the corresponding strata in the region and use thermal conductivity measuring instruments to determine the thermal conductivity data of the carbonate rock formations.
[0044] Step 03: Based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata, establish a thermal conductivity model of the rocks in the study area;
[0045] Step 04: Obtain the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature.
[0046] The step of obtaining the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature includes: using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, to simulate and calculate the un-drilled formation temperature using a one-dimensional heat conduction equation, thereby predicting the well temperature. By using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, and calculating using a one-dimensional heat conduction equation, the un-drilled formation temperature can be simulated and calculated, providing a reliable basis for well temperature prediction.
[0047] The new rock thermal conductivity data and calculation method provided in this embodiment differ from the existing method of assigning the same thermal conductivity to a single rock formation. Instead, it assigns varying rock thermal conductivity to different formations as the depth changes, thus enabling more accurate calculation of formation temperature.
[0048] Example 2:
[0049] Based on the actual temperature and measured thermal conductivity data from the Shunbei 5 well, and combined with previous measured rock thermal conductivity data, this embodiment establishes a rock thermal conductivity model for the Tarim Basin platform area (as shown in Table 1). Overall, it can be seen that the rock thermal conductivity values increase with depth, which is due to the increase in rock density with increasing depth. Furthermore, the relationship between lithology and rock thermal conductivity is also quite close; it can be observed that the thermal conductivity values of Devonian quartz sandstone, Middle-Lower Ordovician carbonate rocks, and Cambrian gypsum-salt rocks are significantly higher.
[0050] Table 1. Thermal conductivity model of rocks in the Tarim Basin area
[0051]
[0052] Based on new rock thermal conductivity data, and considering the impact of compaction on rock thermal conductivity with depth, the temperature of Shunbei 5 was simulated and predicted using a one-dimensional heat conduction equation. The calculation results showed that the simulated temperature results for a single well were in very good agreement with the actual temperature results (e.g., Figure 2 As shown in the figure, the geothermal gradient decreases significantly with increasing stratum depth. The temperature data deviation rate does not exceed 5%.
[0053] To further verify the simulation results, well Shunbei 5 was selected, and the thermal conductivity data from Table 1 were used with Petromod software to reconstruct the burial history of a single well. The simulation results show that (as...) Figure 3As shown in the diagram, by the end of the Permian, the source rocks of the Tayuertus Formation reached the hydrocarbon generation threshold and began continuous hydrocarbon generation, with oil reservoir temperatures around 110℃. This process continued until the end of the Cretaceous, when the source rock temperature reached 163℃ and the oil reservoir temperature was around 135℃. Overall, the hydrocarbon accumulation process can be divided into two phases: the first phase being the Permian, and the second phase being the Jurassic-present period. This aligns with new understandings of hydrocarbon accumulation.
[0054] Example 3:
[0055] This embodiment provides a method for predicting the drilling oil and gas accumulation stages. This method uses the aforementioned drilling temperature prediction method to predict the drilling temperature. Specifically, the drilling temperature is used to reconstruct the drilling burial history, and the drilling oil and gas accumulation stages are predicted based on the reconstructed drilling burial history.
[0056] The methods for predicting drilling temperature include:
[0057] Step 01: Determine whether there is drilling data for the shallow clastic rock strata in the study area; if so, obtain the thermal conductivity data of the clastic rocks based on the drilling data; if not, obtain the thermal conductivity data of the rocks under different porosity conditions after the burial depth changes based on the thermal conductivity data of the rocks measured by the field outcrops in the study area.
[0058] The step of obtaining the thermal conductivity data of clastic rocks based on drilling data includes: obtaining the initial thermal conductivity data of clastic rocks using a one-dimensional heat conduction equation based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value.
[0059] Furthermore, after the step of obtaining the initial clastic rock thermal conductivity data based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value using a one-dimensional heat conduction equation, the method further includes: correcting the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself in the literature, and finally determining the rock thermal conductivity data of different clastic rock formations.
[0060] In summary, for the shallow clastic rock strata in the study area, if there is drilling data nearby, the original static temperature measurement data of the drilling strata can be used, combined with the current regional geothermal heat flow value, and the one-dimensional heat conduction equation can be used to directly calculate the thermal conductivity of the clastic rocks. The abnormal temperature data can be corrected by combining the thermal conductivity data of the clastic rocks themselves in the literature, and finally the thermal conductivity data of different clastic rock strata can be determined.
[0061] For the shallow clastic rock strata in the study area, if there is no drilling data near the study area, the steps for obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the rock thermal conductivity data measured by field outcrops in the study area include: selecting the rock thermal conductivity data measured by field outcrops in the area, while considering the influence of formation porosity on rock thermal conductivity, and obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the porosity calculation formula, according to the ideal condition of uniform pore space distribution, and considering the influence of porosity on thermal conductivity.
[0062] Step 02: Collect field or well samples of the corresponding strata in the study area, and measure the thermal conductivity data of the deep carbonate rock strata by measuring the field or well samples.
[0063] The step of measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples includes: measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples using a thermal conductivity measuring instrument.
[0064] In summary, there is limited data on the thermal conductivity of deep carbonate rock formations in previous studies. It is necessary to collect field or well samples of the corresponding strata in the region and use thermal conductivity measuring instruments to determine the thermal conductivity data of the carbonate rock formations.
[0065] Step 03: Based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata, establish a thermal conductivity model of the rocks in the study area;
[0066] Step 04: Obtain the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature.
[0067] The step of obtaining the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature includes: using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, to simulate and calculate the un-drilled formation temperature using a one-dimensional heat conduction equation, thereby predicting the well temperature. By using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, and calculating using a one-dimensional heat conduction equation, the un-drilled formation temperature can be simulated and calculated, providing a reliable basis for well temperature prediction.
[0068] Finally, based on the predicted drilling temperature, the burial history of a single well is reconstructed, and the drilling oil and gas accumulation period is predicted based on the reconstructed burial history.
[0069] The new rock thermal conductivity data and calculation method provided in this embodiment differ from the existing method of assigning the same thermal conductivity to a rock formation. Instead, it assigns different rock thermal conductivity to different formations as the depth changes. Therefore, it can calculate the formation temperature more accurately. Based on this, it can carry out single-well burial history restoration and more accurately calculate the oil and gas accumulation stages of single wells.
[0070] Example 4:
[0071] This embodiment provides a drilling temperature prediction device 100, such as... Figure 4 As shown, the drilling temperature prediction device 100 includes a judgment module 10, a calculation module 20, an acquisition module 30, a measurement module 40, a model building module 50, and an analysis module 60.
[0072] Module 10 is used to determine whether there is drilling data in the shallow clastic rock formations of the study area;
[0073] The calculation module 20 is used to obtain the thermal conductivity data of clastic rocks based on drilling data; and to obtain the thermal conductivity data of rocks under different porosity conditions after the burial depth changes based on the thermal conductivity data of rocks measured in the field outcrops of the study area.
[0074] The acquisition module 30 is used to collect field or well samples from the corresponding strata in the study area;
[0075] Measurement module 40 is used to measure the thermal conductivity data of deep carbonate rock formations from the field or drilling samples.
[0076] The model building module 50 is used to build a rock thermal conductivity model for the study area based on the rock thermal conductivity of the clastic rock, the rock thermal conductivity data under different porosity conditions after the burial depth changes, and the rock thermal conductivity data of the deep carbonate rock strata.
[0077] The analysis module 60 is used to obtain the formation temperature of the well based on the rock thermal conductivity model of the study area, so as to predict the well temperature.
[0078] The judgment module 10 determines whether there is drilling data in the shallow clastic rock strata of the study area; if so, the calculation module 20 obtains the thermal conductivity data of the clastic rocks based on the drilling data; if not, the calculation module 20 obtains the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth based on the thermal conductivity data of the rocks measured by field outcrops in the study area; the acquisition module 30 collects field or drilling samples of the corresponding strata in the study area; then the measurement module 40 is used to measure the thermal conductivity data of the deep carbonate rock strata by measuring the field or drilling samples; the model building module 50 is used to build a thermal conductivity model of the rocks in the study area based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata; the analysis module 60 is used to obtain the formation temperature of the drilling based on the thermal conductivity model of the rocks in the study area, so as to predict the drilling temperature.
[0079] Furthermore, the calculation module 20 is also used to obtain the initial rock thermal conductivity data of the clastic rocks based on the original static temperature measurement data of the drilling formation and combined with the current regional geothermal heat flow value, using a one-dimensional heat conduction equation.
[0080] Furthermore, the calculation module 20 is also used to correct the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself in the literature, and finally determine the rock thermal conductivity data of different clastic rock strata.
[0081] Furthermore, the calculation module 20 is also used to select rock thermal conductivity data measured from outcrops in the region, while considering the influence of porosity inside the strata on the rock thermal conductivity. Based on the porosity calculation formula, and under the ideal condition of uniform distribution of pore space, and considering the influence of porosity on thermal conductivity, rock thermal conductivity data under different porosity conditions with varying burial depth are obtained.
[0082] Furthermore, the measurement module 40 is also used to measure the thermal conductivity data of deep carbonate rock formations using a thermal conductivity measuring instrument for the field or drilling samples.
[0083] Furthermore, the analysis module 60 is also used to simulate and calculate the un-drilled formation temperature based on the rock thermal conductivity model of the study area and combined with the current regional geothermal heat flow data, using a one-dimensional heat conduction equation, in order to predict the drilling temperature.
[0084] The drilling temperature prediction device 100 provided in this embodiment of the invention uses rock thermal conductivity data corresponding to each rock formation. Based on the variation of rock action with depth, it dynamically analyzes the rock thermal conductivity values of the same rock formation at different depths, thereby establishing a rock thermal conductivity model. This allows for more accurate analysis of formation temperature changes. Simultaneously, using the new rock thermal conductivity model, it performs single-well burial history reconstruction, enabling more accurate prediction of single-well hydrocarbon accumulation stages. This invention can more accurately predict deep formation temperature characteristics and, by utilizing thermal conductivity data, more accurately reconstruct deep burial history processes, providing a basis for determining hydrocarbon accumulation stages.
[0085] Example 5:
[0086] This embodiment provides an electronic device, which may be a mobile phone, computer, or tablet computer, etc., including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the drilling temperature prediction method as described in Embodiment 1. It is understood that the electronic device may further include an input / output (I / O) interface and communication components.
[0087] The processor is used to execute all or part of the steps in the drilling temperature prediction method as described in Embodiment 1. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
[0088] The processor can be implemented as an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field-programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is used to execute the drilling temperature prediction method in Embodiment 1 above.
[0089] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0090] The drilling temperature prediction method based on the above modules includes:
[0091] Step 01: Determine whether there is drilling data for the shallow clastic rock strata in the study area; if so, obtain the thermal conductivity data of the clastic rocks based on the drilling data; if not, obtain the thermal conductivity data of the rocks under different porosity conditions after the burial depth changes based on the thermal conductivity data of the rocks measured by the field outcrops in the study area.
[0092] The step of obtaining the thermal conductivity data of clastic rocks based on drilling data includes: obtaining the initial thermal conductivity data of clastic rocks using a one-dimensional heat conduction equation based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value.
[0093] Furthermore, after the step of obtaining the initial clastic rock thermal conductivity data based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value using a one-dimensional heat conduction equation, the method further includes: correcting the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself in the literature, and finally determining the rock thermal conductivity data of different clastic rock formations.
[0094] In summary, for the shallow clastic rock strata in the study area, if there is drilling data nearby, the original static temperature measurement data of the drilling strata can be used, combined with the current regional geothermal heat flow value, and the one-dimensional heat conduction equation can be used to directly calculate the thermal conductivity of the clastic rocks. The abnormal temperature data can be corrected by combining the thermal conductivity data of the clastic rocks themselves in the literature, and finally the thermal conductivity data of different clastic rock strata can be determined.
[0095] For the shallow clastic rock strata in the study area, if there is no drilling data near the study area, the steps for obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the rock thermal conductivity data measured by field outcrops in the study area include: selecting the rock thermal conductivity data measured by field outcrops in the area, while considering the influence of formation porosity on rock thermal conductivity, and obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the porosity calculation formula, according to the ideal condition of uniform pore space distribution, and considering the influence of porosity on thermal conductivity.
[0096] Step 02: Collect field or well samples of the corresponding strata in the study area, and measure the thermal conductivity data of the deep carbonate rock strata by measuring the field or well samples.
[0097] The step of measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples includes: measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples using a thermal conductivity measuring instrument.
[0098] In summary, there is limited data on the thermal conductivity of deep carbonate rock formations in previous studies. It is necessary to collect field or well samples of the corresponding strata in the region and use thermal conductivity measuring instruments to determine the thermal conductivity data of the carbonate rock formations.
[0099] Step 03: Based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata, establish a thermal conductivity model of the rocks in the study area;
[0100] Step 04: Obtain the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature.
[0101] The step of obtaining the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature includes: using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, to simulate and calculate the un-drilled formation temperature using a one-dimensional heat conduction equation, thereby predicting the well temperature. By using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, and calculating using a one-dimensional heat conduction equation, the un-drilled formation temperature can be simulated and calculated, providing a reliable basis for well temperature prediction.
[0102] The new rock thermal conductivity data and calculation method provided in this embodiment differ from the existing method of assigning the same thermal conductivity to a single rock formation. Instead, it assigns varying rock thermal conductivity to different formations as the depth changes, thus enabling more accurate calculation of formation temperature.
[0103] Example 6:
[0104] This embodiment also provides a computer-readable storage medium. The functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
[0105] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.
[0106] The aforementioned storage media include: flash memory, hard disks, multimedia cards, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disks, optical discs, servers, APP application stores, and various other media capable of storing program verification codes, on which computer programs are stored. When the computer program is executed by a processor, it can implement the following method steps:
[0107] Step 01: Determine whether there is drilling data for the shallow clastic rock strata in the study area; if so, obtain the thermal conductivity data of the clastic rocks based on the drilling data; if not, obtain the thermal conductivity data of the rocks under different porosity conditions after the burial depth changes based on the thermal conductivity data of the rocks measured by the field outcrops in the study area.
[0108] The step of obtaining the thermal conductivity data of clastic rocks based on drilling data includes: obtaining the initial thermal conductivity data of clastic rocks using a one-dimensional heat conduction equation based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value.
[0109] Furthermore, after the step of obtaining the initial clastic rock thermal conductivity data based on the original static temperature measurement data of the drilling formation and the current regional geothermal heat flow value using a one-dimensional heat conduction equation, the method further includes: correcting the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself in the literature, and finally determining the rock thermal conductivity data of different clastic rock formations.
[0110] In summary, for the shallow clastic rock strata in the study area, if there is drilling data nearby, the original static temperature measurement data of the drilling strata can be used, combined with the current regional geothermal heat flow value, and the one-dimensional heat conduction equation can be used to directly calculate the thermal conductivity of the clastic rocks. The abnormal temperature data can be corrected by combining the thermal conductivity data of the clastic rocks themselves in the literature, and finally the thermal conductivity data of different clastic rock strata can be determined.
[0111] For the shallow clastic rock strata in the study area, if there is no drilling data near the study area, the steps for obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the rock thermal conductivity data measured by field outcrops in the study area include: selecting the rock thermal conductivity data measured by field outcrops in the area, while considering the influence of formation porosity on rock thermal conductivity, and obtaining the rock thermal conductivity data under different porosity conditions with varying burial depth based on the porosity calculation formula, according to the ideal condition of uniform pore space distribution, and considering the influence of porosity on thermal conductivity.
[0112] Step 02: Collect field or well samples of the corresponding strata in the study area, and measure the thermal conductivity data of the deep carbonate rock strata by measuring the field or well samples.
[0113] The step of measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples includes: measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples using a thermal conductivity measuring instrument.
[0114] In summary, there is limited data on the thermal conductivity of deep carbonate rock formations in previous studies. It is necessary to collect field or well samples of the corresponding strata in the region and use thermal conductivity measuring instruments to determine the thermal conductivity data of the carbonate rock formations.
[0115] Step 03: Based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata, establish a thermal conductivity model of the rocks in the study area;
[0116] Step 04: Obtain the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature.
[0117] The step of obtaining the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature includes: using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, to simulate and calculate the un-drilled formation temperature using a one-dimensional heat conduction equation, thereby predicting the well temperature. By using the rock thermal conductivity model of the study area, combined with current regional geothermal heat flow data, and calculating using a one-dimensional heat conduction equation, the un-drilled formation temperature can be simulated and calculated, providing a reliable basis for well temperature prediction.
[0118] The new rock thermal conductivity data and calculation method provided in this embodiment differ from the existing method of assigning the same thermal conductivity to a single rock formation. Instead, it assigns varying rock thermal conductivity to different formations as the depth changes, thus enabling more accurate calculation of formation temperature.
[0119] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. It will be clearly understood by those skilled in the art that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0121] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.
[0122] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner.
[0123] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.
[0124] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.
[0125] In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, top, bottom, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the figures). If the specific posture changes, the directional indication will also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.
[0126] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0127] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for predicting drilling temperature, characterized in that, include: Determine whether there is drilling data for the shallow clastic rock strata in the study area; if so, obtain the thermal conductivity data of the clastic rocks based on the drilling data; if not, obtain the thermal conductivity data of the rocks under different porosity conditions after the change with burial depth based on the thermal conductivity data of the rocks measured by the field outcrops in the study area. Field or well samples were collected from the corresponding strata in the study area, and the thermal conductivity data of the deep carbonate rock strata were obtained by measuring the field or well samples. A rock thermal conductivity model for the study area was established based on the thermal conductivity of the clastic rocks, the thermal conductivity data of the rocks under different porosity conditions after changes in burial depth, and the thermal conductivity data of the deep carbonate rock strata. The formation temperature of the well is obtained based on the rock thermal conductivity model of the study area to predict the well temperature; The step of obtaining the thermal conductivity data of clastic rocks based on drilling data includes: obtaining the initial thermal conductivity data of clastic rocks based on the original static temperature measurement data of the drilling formation and the geothermal heat flow value of the study area using a one-dimensional heat conduction equation. After the step of obtaining the initial clastic rock thermal conductivity data based on the original static temperature measurement data of the drilling formation and the geothermal heat flow value of the study area using a one-dimensional heat conduction equation, the method further includes: correcting the abnormal temperature data in the initial clastic rock thermal conductivity data by combining the rock thermal conductivity data of the clastic rock itself, and finally determining the rock thermal conductivity data of different clastic rock formations. The steps for obtaining rock thermal conductivity data under different porosity conditions with varying burial depth based on rock thermal conductivity data measured from outcrops in the study area include: selecting rock thermal conductivity data measured from outcrops in the area, considering the influence of stratum porosity on rock thermal conductivity, and obtaining rock thermal conductivity data under different porosity conditions with varying burial depth based on the porosity calculation formula, according to the ideal condition of uniform pore space distribution, and considering the influence of porosity on thermal conductivity. The step of measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples includes: measuring the thermal conductivity data of deep carbonate rock formations from the field or well samples using a thermal conductivity measuring instrument. The step of obtaining the formation temperature of the well based on the rock thermal conductivity model of the study area to predict the well temperature includes: based on the rock thermal conductivity model of the study area and combined with the geothermal heat flow data of the study area, using a one-dimensional heat conduction equation to simulate and calculate the formation temperature of the un-drilled well to predict the well temperature.
2. A method for predicting drilling oil and gas accumulation stages, characterized in that, The drilling temperature is predicted using the drilling temperature prediction method described in claim 1. Based on the drilling temperature, the drilling burial history is reconstructed, and the drilling oil and gas accumulation period is predicted based on the reconstructed drilling burial history.
3. A drilling temperature prediction device for implementing the drilling temperature prediction method of claim 1, characterized in that, include: The judgment module is used to determine whether there is drilling data in the shallow clastic rock formations of the study area; The calculation module is used to obtain the thermal conductivity data of clastic rocks based on drilling data; Based on the rock thermal conductivity data measured by field outcrops in the study area, rock thermal conductivity data under different porosity conditions with varying burial depth were obtained. The data acquisition module is used to collect field or well samples from the corresponding strata in the study area. The measurement module is used to measure the thermal conductivity data of deep carbonate rock formations from the field or drilling samples. The model building module is used to build a rock thermal conductivity model for the study area based on the rock thermal conductivity of the clastic rocks, the rock thermal conductivity data under different porosity conditions after the burial depth changes, and the rock thermal conductivity data of the deep carbonate rock strata. The analysis module is used to obtain the formation temperature of the well based on the rock thermal conductivity model of the study area, so as to predict the well temperature.
4. An electronic device, characterized in that, The system includes a memory and a processor, the memory being used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the drilling temperature prediction method as described in claim 1.
5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, is used to implement the drilling temperature prediction method as described in claim 1.