Gene quantitative microorganism oil and gas exploration method based on mathematical statistics

Abstract

The application discloses a gene quantitative microorganism oil and gas exploration method based on mathematical statistics, which comprises the following steps: step 1, field sampling and sample transportation, sampling of cuttings and produced fluid and transporting to the laboratory in the fastest way. The beneficial effect is that the application discards artificial additives and instead uses the inherent microbial community in the stratum which coexists with the oil and gas environment as a 'natural tracer', which fundamentally eliminates the pollution risk and realizes absolute green environmental protection; at the same time, since no expensive reagent is needed, the main cost is concentrated in the later analysis, so that large-scale and high-frequency application becomes possible; at the same time, the microbial community structure in the application can respond and evolve in real time with the reservoir environment (such as fluid change), and through regular sampling analysis, the method can continuously capture the dynamic change of the'microbial fingerprint', and realize long-term and high-time-effect tracking of the produced fluid profile, interwell connectivity and water drive front.

Description

Technical Field

[0001] This invention relates to the field of microbial oil and gas exploration technology, and in particular to a gene-quantitative microbial oil and gas exploration method based on mathematical statistics. Background Technology

[0002] Oil and gas exploration refers to geological surveys, geophysical exploration, drilling activities, and other related activities conducted to identify exploration areas or determine oil and gas reserves. Oil and gas exploration is the first crucial step in oil and gas extraction, forming the foundation of oil and gas extraction engineering. Its purpose is to find and identify oil and gas resources, utilizing various exploration methods to understand underground geological conditions, recognize conditions related to oil generation, storage, migration, accumulation, and preservation, comprehensively evaluate oil and gas prospects, identify favorable areas for oil and gas accumulation, locate oil and gas traps, determine the area of ​​oil and gas fields, and clarify the characteristics and production capacity of oil and gas reservoirs. Detecting microbial genes in the soil sample to study the relationship between microbial anomalies in near-surface soil layers and deep underground oil and gas reservoirs is a promising oil and gas exploration method.

[0003] Current microbial chemical exploration technology faces three major bottlenecks that restrict its large-scale, safe, and long-term application: 1. Economic and scalability bottleneck: It relies on the injection of high-cost radioactive or chemical tracers, which need to be re-injected for each exploration. This "one-time" high-investment model results in high single-point exploration costs, making it difficult to conduct large-scale, grid-based continuous monitoring within the oilfield, leading to poor economic benefits and hindering large-scale promotion; 2. Environmental safety and sustainability bottleneck: The tracers used are radioactive or chemically toxic, posing a risk of leakage during downhole migration, potentially causing irreversible pollution to valuable aquifers. This contradicts the current green and environmentally friendly energy development concept and also brings potential regulatory and social responsibility risks; 3. Technical effectiveness and continuity bottleneck: Artificially injected tracers have a short effective period and rapid decay, only providing short-term "snapshot" data. This makes it impossible to achieve long-term, continuous monitoring of reservoir dynamics (such as fluid front movement and dominant channel evolution), resulting in data inconsistency and difficulty in supporting refined reservoir management and development strategy adjustments. Summary of the Invention

[0004] The purpose of this invention is to provide a quantitative gene-based microbial oil and gas exploration method based on mathematical statistics to solve the above-mentioned problems.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] A quantitative gene-based method for microbial hydrocarbon exploration based on mathematical statistics includes the following steps:

[0007] Step 1: On-site sampling and sample transportation. Take samples of rock cuttings and produced fluids and transport them to the laboratory as quickly as possible.

[0008] Step 2: Laboratory processing and sample sequencing, including DNA extraction, DNA purification, PCR amplification, and detection of microbial genes in all samples;

[0009] Step 3: Big data analysis and labeling of sequencing results, cluster analysis of sequencing results, and statistical analysis of bacterial species and abundance in each sample;

[0010] Step 4: Apply modules and auxiliary decision-making to construct production profiles, analyze inter-well connectivity, conduct high-dynamic monitoring, trace crude oil leaks, and provide reservoir optimization suggestions.

[0011] Furthermore, in step 1, when sampling rock cuttings, samples are taken from a vibrating screen at the wellhead, with samples collected every 3 meters in vertical wells and every 15 or 30 meters in horizontal wells; at the same time, self-sealing bags are used for encrypted sampling, with the bags labeled with the corresponding well name, sampling time, and formation depth, and each self-sealing bag has a capacity of 50-200 grams.

[0012] Furthermore, in step 1, when sampling the produced fluid, a conical container is used for intensive sampling to collect the fluid directly from the wellhead, with each container containing 23 to 45 ml, not exceeding 70 ml.

[0013] Furthermore, in step 1, after sampling the rock fragments and the produced fluid, the temperature is immediately lowered to -10°C (the minimum temperature at which microorganisms remain active), and a buffer is added to prevent bacterial growth.

[0014] Furthermore, in step 2, the sequencing of the sample is performed using a Miseq gene sequencer.

[0015] Furthermore, in implementing step 1, the following measures were taken to address the problem of low biomass of rock debris microorganisms: first, optimizing sample collection to maintain microbial activity; second, pretreatment to increase DNA concentration in the sample; and third, optimizing the extraction process to ensure high-quality DNA extraction, amplification, and sequencing, laying the foundation for subsequent stratigraphic microbial characterization.

[0016] Furthermore, in specific implementation, step 3 involves using sequencing results to construct a stratigraphic baseline, which is a columnar stacking diagram of the bacterial species and their abundance contained in each stratum or horizontal segment. The vertical axis represents the stratigraphic depth, the horizontal axis represents the bacterial species abundance, and different bacterial species are represented by different colors.

[0017] Furthermore, in step 4, auxiliary decision-making and production dynamic monitoring are carried out through microbial formation response profiles, such as fine formation division, production profile monitoring, fracture height monitoring, and well connectivity assessment.

[0018] Furthermore, a refined stratigraphic subdivision method was established, capable of dividing the same 20-meter interval into three more sub-layers than well logging curves, with a resolution of up to 1-2 meters. This achieves a more refined subdivision and description of shale formations. This method is simple to operate, environmentally friendly, and low-cost, providing a more scientific basis and technical support for shale oil and gas development. During production profile monitoring, cuttings and production fluids are sampled and sequenced. Big data algorithms are used to trace the origin of production bacteria by analyzing the composition ratio of characteristic microbial species in each formation, obtaining the probability that the production bacteria originate from each formation. This information is used to construct a production profile, representing the contribution rate of each formation to production. The production profile map... The sum of formation depths with contribution rates greater than the average contribution rate can be considered as fracture height. The average contribution rate threshold of the formation is calculated and represented by a dashed line. Formations with contribution rates exceeding the dashed line are considered effective layers. The sum of formation depths with contribution rates exceeding the threshold around the target layer is the effective fracture height. If DNA sequences from the formations at both ends of the interlayer are found in the product fluid DNA sequencing results, it can be determined that the fracture has passed through the interlayer. Inter-well connectivity assessment involves principal coordinate analysis of the product fluid sample sequencing results. Points that are closer together indicate a stronger correlation. The species abundance heatmap is clustered based on the similarity of sample abundance. The closer the distance and the shorter the branch length, the more similar the species composition and abundance of the samples.

[0019] The beneficial effects of this invention are as follows:

[0020] 1. This invention abandons artificial additives and instead utilizes the inherent microbial community in the formation that coexists with the oil and gas environment as a "natural tracer," which fundamentally eliminates the risk of pollution and achieves absolute green environmental protection. At the same time, since no expensive reagents are required, the main cost is concentrated in the later analysis, making large-scale and high-frequency application possible.

[0021] 2. In this invention, the microbial community structure responds and evolves in real time with the reservoir environment (such as fluid changes). Through periodic sampling and analysis, this method can continuously capture the dynamic changes of this "microbial fingerprint", which is like installing a real-time dynamic monitoring system for the reservoir, realizing long-term and high-time-efficiency tracking of production profile, inter-well connectivity, water drive front, etc.

[0022] 3. This invention does not rely on a single microbial indicator, but obtains multidimensional big data (species composition, abundance, functional genes) of the entire microbial community through high-throughput sequencing. Combined with bioinformatics analysis, it can draw a deeper "microbial geochemical map", upgrading the judgment results from a simple "presence or absence" judgment to a precise source tracing and systematic insight into reservoir dynamics, connectivity, and even leakage sources.

[0023] 4. The application modules (production profile, connectivity analysis, etc.) constructed by this invention directly serve production optimization, providing direct data support for key decisions such as adjusting injection and production schemes, blocking advantageous channels, and improving recovery rate, extending the technical value to the entire life cycle of reservoir management, and realizing a closed loop from monitoring to decision-making. Attached Figure Description

[0024] Figure 1 This is a flowchart of a gene-quantitative microbial oil and gas exploration method based on mathematical statistics, as described in this invention. Detailed Implementation

[0025] A quantitative gene-based method for microbial hydrocarbon exploration based on mathematical statistics includes the following steps:

[0026] Step 1: On-site sampling and sample transportation. Take samples of rock cuttings and produced fluids and transport them to the laboratory as quickly as possible.

[0027] Step 2: Laboratory processing and sample sequencing, including DNA extraction, DNA purification, PCR amplification, and detection of microbial genes in all samples;

[0028] Step 3: Big data analysis and labeling of sequencing results, cluster analysis of sequencing results, and statistical analysis of bacterial species and abundance in each sample;

[0029] Step 4: Apply modules and auxiliary decision-making to construct production profiles, analyze inter-well connectivity, conduct high-dynamic monitoring, trace crude oil leaks, and provide reservoir optimization suggestions.

[0030] In this embodiment, when sampling rock cuttings in step 1, samples are taken from a vibrating screen at the wellhead, with samples collected every 3 meters in vertical wells and every 15 or 30 meters in horizontal wells; at the same time, self-sealing bags are used for encrypted sampling, with the bags labeled with the corresponding well name, sampling time, and formation depth, and each self-sealing bag has a capacity of 50-200 grams.

[0031] In this embodiment, when sampling the produced fluid in step 1, a conical container is used for intensive sampling to collect the fluid directly from the wellhead, with each container containing 23 to 45 ml, not exceeding 70 ml.

[0032] In this embodiment, after sampling the rock fragments and the produced fluid in step 1, the temperature is immediately lowered to -10°C (the minimum temperature at which microorganisms remain active), and a buffer is added to prevent bacterial growth.

[0033] In this embodiment, step 2 involves sequencing the sample using a Miseq gene sequencer.

[0034] In this embodiment, when implementing step 1, the problem of low biomass of rock debris microorganisms is addressed from the following aspects: first, optimizing sample collection to maintain microbial activity; second, pretreatment to increase DNA concentration in the sample; and third, optimizing the extraction process to ensure high-quality DNA extraction, amplification, and sequencing, laying the foundation for subsequent stratigraphic microbial characterization.

[0035] In this embodiment, step 3 is implemented by using sequencing results to construct a stratigraphic baseline, which is a columnar stacking diagram of the bacterial species and their abundance contained in each stratum or horizontal segment. The vertical axis represents the stratum depth, the horizontal axis represents the bacterial species abundance, and different bacterial species are represented by different colors.

[0036] In this embodiment, step 4 involves using microbial formation response profiles for auxiliary decision-making and production dynamic monitoring, such as fine formation division, production profile monitoring, fracture height monitoring, and well connectivity assessment.

[0037] In this embodiment, a fine formation subdivision method was established, which can subdivide the same 20-meter interval into 3 more sub-layers than well logging curves, with a resolution of up to 1-2 meters. This achieves a more refined subdivision and description of shale formations. This method is simple to operate, environmentally friendly, and low-cost, and can provide a more scientific basis and technical support for shale oil and gas development. During production profile monitoring, cuttings and production fluids are sampled and sequenced. By using big data algorithms to trace the origin of production bacteria through the composition ratio of characteristic bacterial species in each formation, the probability of production bacteria originating from each formation is obtained. This is used to construct a production profile, representing the contribution rate of each formation to production. The production profile map shows... The sum of formation depths with contribution rates greater than the average contribution rate can be considered as fracture height. The average contribution rate threshold of the formation is calculated and represented by a dashed line. Formations with contribution rates exceeding the dashed line are considered effective layers. The sum of formation depths with contribution rates exceeding the threshold around the target layer is the effective fracture height. If DNA sequences from the formations at both ends of the interlayer are found in the product fluid DNA sequencing results, it can be determined that the fracture has passed through the interlayer. Inter-well connectivity assessment involves principal coordinate analysis of the product fluid sample sequencing results. Points that are closer together indicate a stronger correlation. The species abundance heatmap is clustered based on the similarity of sample abundance. The closer the distance and the shorter the branch length, the more similar the species composition and abundance of the samples.

[0038] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quantitative gene-based microbial oil and gas exploration method based on mathematical statistics, characterized in that: It includes the following steps: Step 1: On-site sampling and sample transportation. Take samples of rock cuttings and produced fluids and transport them to the laboratory as quickly as possible. Step 2: Laboratory processing and sample sequencing, including DNA extraction, DNA purification, PCR amplification, and detection of microbial genes in all samples; Step 3: Big data analysis and labeling of sequencing results, cluster analysis of sequencing results, and statistical analysis of bacterial species and abundance in each sample; Step 4: Apply modules and auxiliary decision-making to construct production profiles, analyze inter-well connectivity, conduct high-dynamic monitoring, trace crude oil leaks, and provide reservoir optimization suggestions.

2. The method for quantitative microbial oil and gas exploration based on mathematical statistics according to claim 1, characterized in that: In step 1, when sampling rock cuttings, samples are taken from a vibrating screen at the wellhead. Samples are collected every 3 meters in vertical wells and every 15 or 30 meters in horizontal wells. At the same time, self-sealing bags are used for encrypted sampling. The bags are labeled with the corresponding well name, sampling time, and formation depth. Each self-sealing bag has a capacity of 50-200 grams.

3. The method for quantitative microbial oil and gas exploration based on mathematical statistics according to claim 1, characterized in that: In step 1, when sampling the produced fluid, a conical container is used for intensive sampling to collect the fluid directly from the wellhead, with each container containing 23 to 45 ml, not exceeding 70 ml.

4. The method for quantitative microbial oil and gas exploration based on mathematical statistics according to claim 1, characterized in that: In step 1, after sampling the rock fragments and the produced fluid, the temperature is immediately lowered to -10°C (the minimum temperature at which microorganisms remain active), and a buffer is added to prevent bacterial growth.

5. The method for quantitative microbial oil and gas exploration based on mathematical statistics according to claim 1, characterized in that: In step 2, the sequencing of the sample is performed using a Miseq gene sequencer.

6. The method for quantitative microbial oil and gas exploration based on mathematical statistics as described in claim 1, characterized in that: In specific implementation, step 3 involves using sequencing results to construct a stratigraphic baseline, which is a columnar stacking diagram of the bacterial species and their abundance contained in each stratum or horizontal segment. The vertical axis represents the stratum depth, the horizontal axis represents the bacterial species abundance, and different bacterial species are represented by different colors.

7. The method for quantitative microbial oil and gas exploration based on mathematical statistics according to claim 1, characterized in that: In step 4, auxiliary decision-making and production dynamic monitoring are carried out through microbial formation response profiles, such as fine formation division, production profile monitoring, fracture height monitoring, and well connectivity assessment.

8. The quantitative gene-based microbial oil and gas exploration method based on mathematical statistics according to claim 7, characterized in that: A refined stratigraphic subdivision method was established, capable of dividing the same 20-meter interval into three more sub-layers than well logging curves, with a resolution of up to 1-2 meters. This achieves a more refined subdivision and description of shale formations. The method is simple to operate, environmentally friendly, and low-cost, providing a more scientific basis and technical support for shale oil and gas development. During production profile monitoring, cuttings and production fluids are sampled and sequenced. Big data algorithms are used to trace the origin of production bacteria by analyzing the composition ratio of characteristic microorganisms in each formation, obtaining the probability that the production bacteria originate from each formation. This information is used to construct a production profile, representing the contribution rate of each formation to production. The contribution rate in the production profile map is shown below. The sum of formation depths with a contribution rate greater than the average contribution rate can be considered as the fracture height. The average contribution rate threshold of the formation is calculated and represented by a dashed line. The formation with a contribution rate exceeding the dashed line is considered an effective layer. The sum of formation depths with a contribution rate exceeding the threshold around the target layer is considered as the effective fracture height. At the same time, if DNA sequences from the formations at both ends of the interlayer are found in the production fluid DNA sequencing results, it can be determined that the fracture has passed through the interlayer. The inter-well connectivity assessment involves principal coordinate analysis of the production fluid sample sequencing results. Points that are closer together indicate a stronger correlation. The species abundance heatmap is clustered based on the abundance similarity of the samples. The closer the distance and the shorter the branch length, the more similar the species composition and abundance of the samples are.

Images