CN105629327A - Lithogenous phase quantitative characterization method for weak adhesion bonds and deep compact sandstone reservoirs - Google Patents
Lithogenous phase quantitative characterization method for weak adhesion bonds and deep compact sandstone reservoirs Download PDFInfo
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Abstract
The invention provides a lithogenous phase quantitative characterization method for weak adhesion bonds and deep compact sandstone reservoirs, and belongs to the field of petroleum exploration development. The method comprises following steps of (1) quantitatively calculating the vision corrosion rate and the vision compaction rate via casting thin section; 2) determining the type of a single-well lithogenous phase via a rock core, common thin sheets and a scanning electron microscope by combining the vision corrosion rate and the vision compaction rate; 3) screening out a logging sensitive parameter and by use of the BP neural network technology, establishing a corresponding relation between the vision corrosion rate, the vision compaction rate and the logging sensitive parameter so as to obtain a logging quantitative explanation result of the lithogenous phase; and 4) screening out a seismic inversion attribute parameter and by combining a seismic inversion data body, establishing a corresponding relation between the vision corrosion rate, the vision compaction rate and the seismic inversion attribute parameter so as to obtain a panel prediction result of the lithogenous phase. According to the invention, by combining the rock core, the logging and seismic data, quantitative characterization and prediction of the lithogenous phase are achieved; and the method is an important basis for determining a high-quality reservoir in deep compact sandstone and has a wide application prospect.
Description
Technical field
The present invention relates to petroleum exploration & development geology field, it is a kind of become petrofacies quantitatively characterizing method for weak cementing, deep layer Sandstone Gas Reservoir.
Background technology
To reservoir study, it is more meticulous that diagenesis compares sediment-filled phase. Under certain structure and depositional setting, particularly for the origin cause of formation and the distribution research of reservoir favourable in tight sand, diagenetic impact is bigger, so becoming petrofacies quantitative examination more to highlight its importance.
The one-tenth petrofacies quantitatively characterizing method that one packages should pointed, scientific and practicality. Become the research of petrofacies mainly to utilize the method and technologies such as the observation of rock core, thin slice, scanning electron microscope, cathodeluminescence to carry out the qualitative analysis that individual well becomes petrofacies at present, become the big difficult problem laterally predicted between petrofacies quantitative examination and well and become high-quality reservoir prediction in deep layer tight sand. The quantitative examination of petrofacies is become mainly to there is following problem with laterally prediction research: (1) adopts which kind of integrated approach and technology to realize into the quantitative examination of petrofacies; (2) Rock thin section analysis data is discontinuous, how to select different one-tenth petrofacies to combine with well logging sensitive parameter, it is achieved individual well continuous print becomes petrofacies quantitative interpretation; (3) owing to sandstone reservoir lateral variation is fast, Diagn is complicated, the integrated informations such as rock core, well logging, earthquake (well shake combines) how are utilized to realize becoming between well the transverse direction of petrofacies to predict? therefore, lack at present and become the quantitative comprehensive characterization method of petrofacies for weak cementing, deep layer Sandstone Gas Reservoir.
There are forefathers to propose depending on corrosion rate, depending on compacting rate and its, reservoir properties contribution be there is no corresponding relation, it are not suitable as into the foundation that petrofacies quantitatively divide. But under the weak cementing prerequisite of reservoir, depending on corrosion rate, the degree representing the relative corrosion of reservoir and compacting depending on compacting rate, when looking compacting rate and be certain, the relative development degree of secondary pores is represented depending on the height of corrosion rate, namely can be used as the foundation that diagenesis quantitatively divides mutually, also it is the basis of comparative good-quality reservoir prediction.
The changes such as the rock composition that causes due to Diagn difference, pore texture have visibly different response characteristic on well-log information, and well-log information has continuous recording feature, therefore the log parameter filtered out and become petrofacies relevant, petrofacies will be become corresponding with log parameter, set up continuous print and become petrofacies log interpretation model. Petrofacies well logging discrimination model is become both at home and abroad at present, as Lai Jin etc. will become petrofacies and log parameter intuitively to be reacted by spider diagram mainly through log parameter and mathematics method establishment; Haitao Zhangs etc. differentiate the linear multiple differentiation relation set up log parameter with become petrofacies by Bayes, thus carry out into the continuous and quantitative identification of petrofacies; By gamma curve, TurnerR identifies that clay film is cemented to petrofacies; Jing Cheng, Song Ziqi etc., in conjunction with multiple diagenesis quantitative factor and well logging information, utilize the individual well of gray theory comprehensive evaluation quantitative analysis tight gas reservoir to become petrofacies. The present invention is directed to weak cementing Sandstone Gas Reservoir, filter out and corrosion, well logging sensitive parameter that compacting effort is relevant, such as natural gamma, neutron, interval transit time, density and resistivity, utilize BP nerual network technique to set up depending on corrosion rate, look compacting rate and the relation of well logging sensitive parameter, and then realize into the Well log quantitative explanation of petrofacies.
Due to the explanation precision problem of seismic data, utilize earthquake means at present both at home and abroad, the plane prediction achievement becoming petrofacies is less. Utilize seismic stratigraphy and the petrologic petrological means of earthquake such as MathisenME (1997), rough distinguishes porous diagenetic facies zones and fine and close diagenetic facies zones, establishes into the earthquake-stratigraphic model of petrofacies; Zeng Hongliu (2013) etc. analyze the relation of calcite content and shale relative content and wave resistance, have identified two kinds of earthquakes and become petrofacies (calcitic cementation phase and argillaceous agglutination phase), and open up cloth in conjunction with sediment-filled phase and carry out the prediction that earthquake becomes petrofacies. The present invention adopts " well-shake " method of combining, sets up and predicts depending on corrosion rate, the plane that realizes into petrofacies depending on the relation of compacting rate with seismic inversion attribute.
Summary of the invention
The present invention " a kind of become petrofacies quantitatively characterizing method for weak cementing, deep layer Sandstone Gas Reservoir " is in conjunction with the achievement in research of forefathers, propose the method utilizing rock core, well logging and seismic data to combine, taking depending on corrosion rate with look compacting rate as middle quantitative factor, set up rock core and become the contact between petrofacies, well logging sensitive parameter and seismic inversion attribute, it is achieved become quantitatively characterizing and the plane prediction of petrofacies. It is characterized in that:
The present invention is realized by following step:
1) determine depending on corrosion rate by casting body flake quantitative Analysis, look compacting rate;
2) by core description, ordinary sheet analysis, scanning electron microscopic observation, in conjunction with depending on corrosion rate, determine individual well diagenesis facies type depending on the compacting rate intensity criteria for classifying;
3) filter out the log parameter to corrosion, compacting effort sensitivity and utilize BP nerual network technique, set up depending on corrosion rate, look compacting rate and the corresponding relation of well logging sensitive parameter, obtain into petrofacies Well log quantitative explanation and recognition result;
Step 3) the well logging sensitive parameter that filters out is: DT (sound wave), DEN (density), GR (natural gamma), CNCF (neutron), RT (resistivity);
4) filtering out the seismic inversion attribute sensitive parameter to corrosion, compacting effort sensitivity and in conjunction with seismic inversion data volume, set up depending on corrosion rate, look compacting rate and the corresponding relation of seismic inversion attribute sensitive parameter, the plane obtaining into petrofacies predicts the outcome;
Step 4) in, the seismic inversion attribute sensitive parameter filtered out is p-wave impedance (Ip), S-wave impedance (Is), P-S wave velocity ratio (Vp/Vs), and the planar distribution of petrofacies is predicted in the seismic inversion attribute section depending on corrosion rate, depending on compacting rate.
Accompanying drawing illustrates:
Fig. 1 is the graphs of a relation of all kinds of one-tenth petrofacies with each parameter of well logging
Fig. 2 is that petrofacies schema is logged well in the differentiation of BP neural network method
Fig. 3 is seismic inversion parameter and diagenesis intensity parameter dependency graph
Fig. 4 is that rock core becomes petrofacies and well logging interpretation diagenesis phase comparison diagram
Embodiment
The present invention is by rock core, well logging and seismic data, set up a kind of for weak cementing, deep layer Sandstone Gas Reservoir becomes petrofacies quantitatively characterizing method, is that unconventionaloil pool reservoir study provides a set of effective characterization scheme.
The present invention is realized by following step:
1) determine depending on corrosion rate by casting body flake quantitative Analysis, look compacting rate;
Step 1) propose
Initial pore volume=20.91+22.9/ (��1/��2) (�� 1, �� 2 represent the particle diameter that on granularity summation curve, 25% and 75% place is corresponding respectively)
2) by core description, ordinary sheet analysis, scanning electron microscopic observation, in conjunction with depending on corrosion rate, determine individual well diagenesis facies type depending on the compacting rate intensity criteria for classifying;
3) filter out the log parameter to corrosion, compacting effort sensitivity and utilize BP nerual network technique, set up depending on corrosion rate, look compacting rate and the corresponding relation of well logging sensitive parameter, obtain into petrofacies Well log quantitative explanation and recognition result;
Step 3) the well logging sensitive parameter that filters out is: DT (sound wave), DEN (density), GR (natural gamma), CNCF (neutron), RT (resistivity), wherein the increase effect of the porosity that corrosion is caused by sound wave and density is comparatively responsive, natural gamma and neutron are comparatively responsive to compacting effort, resistivity can reflect the micropore structure of reservoir to a certain extent, and natural gamma can react the lithology of rock in stratum, shale index etc.;
Step 3) the BP nerual network technique that utilizes that proposes carries out into petrofacies Well log quantitative explanation, first to the well logging sensitive parameter filtered out and corresponding depending on corrosion rate, be normalized depending on compacting rate, with remove variable unit between each well unified bring unfavorable, also random deviation and systematic error that logging instrumentation causes is eliminated, ensure that log data can reflect stratum characteristic more accurately, and due to the ununified impact caused of logging suite between each well. Next choose by thin slice quantitative Analysis depending on corrosion rate, look compacting rate and the well logging sensitive parameter of correspondence as train samples, sensitive parameter will be logged well as input neuron, taking depending on corrosion rate, look compacting rate as decision attribute, as output neuron, when training error is less than 0.1%, network is set up and is achieved the goal, and then carries out full well section and become petrofacies Well log quantitative explanation;
4) filtering out the seismic inversion attribute sensitive parameter to corrosion, compacting effort sensitivity and in conjunction with seismic inversion data volume, set up depending on corrosion rate, look compacting rate and the corresponding relation of seismic inversion attribute sensitive parameter, the plane obtaining into petrofacies predicts the outcome;
Step 4) in, the seismic inversion attribute sensitive parameter filtered out is p-wave impedance (Ip), S-wave impedance (Is), P-S wave velocity ratio (Vp/Vs); Respectively will depending on corrosion rate, do multivariate linear regression depending on compacting rate and Ip, Is, Vp/Vs parameter, regression equation is imported seismic inversion data volume depending on corrosion rate, the inverting attribute of looking compacting rate, in conjunction with the diagenesis intensity criteria for classifying, the distribution range becoming petrofacies is drawn a circle to approve by the superimposed attribute section depending on corrosion rate, depending on compacting rate, finally predicts into the planar distribution of petrofacies.
Embodiment
West Lake Depression flower port group is a set of cementing weak deep layer Sandstone Gas Reservoir, reservoir has strong nonuniformity, construction of stable in work area, the type of sedimentary micro is clear and definite, but the impact by corrosion, compacting effort, become the type complexity of petrofacies various, and when few well, vertical become petrographic interpretation and prediction work difficulty relatively big with plane, adopt the method for the present invention, dissimilar one-tenth petrofacies can be quantitatively described and predict.
First add up all and get core section core wafer, wall heart thin slice more than 600, carry out the microtexture of casting body flake and the observation of pore evolution and quantitative description and work, calculate depending on corrosion rate and look compacting rate.
Initial pore volume=20.91+22.9/ (��1/��2) (�� 1, �� 2 represent the particle diameter that on granularity summation curve, 25% and 75% place is corresponding respectively)
2nd step, by core description, ordinary sheet analysis, scanning electron microscopic observation, the reservoir diagenetic intensity criteria for classifying is determined in conjunction with work area overview, as shown in the table, divide out four kinds of individual well diagenesis facies types: middle corrosion-in be compacted into petrofacies, middle corrosion-Zhong Qiang compacting diagenesis phase, strong corrosion-strong compacting diagenesis phase, in strong corrosion-strong compacting diagenesis phase.
| Compacting effort intensity | Depending on compacting rate/% | Corrosion intensity | Depending on corrosion rate/% |
| Suppress reality | >75 | Strong corrosion | >75 |
| In suppress reality | 65��75 | In strong corrosion | 60��75 |
| Middle compacting | 30��65 | Middle corrosion | 30��60 |
| Weak compacting | <30 | Weak corrosion | >30 |
3rd step, filter out the log parameter of corrosion, compacting effort sensitivity: DT (sound wave), DEN (density), GR (natural gamma), CNCF (neutron), RT (resistivity), become petrofacies to have good corresponding relation with the analysis (Fig. 1) of well logging sensitive parameter is known by difference.
4th step, to the well logging sensitive parameter filtered out and corresponding look corrosion rate, it is normalized depending on compacting rate, choose and look corrosion rate by thin slice quantitative Analysis, depending on the well logging sensitive parameter of compacting rate and correspondence as train samples, sensitive parameter will be logged well as input neuron, to look corrosion rate, it is decision attribute depending on compacting rate, as output neuron, when training error is less than 0.1%, network is set up and is achieved the goal (Fig. 2), and then carry out the Well log quantitative explanation that full well section becomes petrofacies, continuous print diagenesis facies evolution feature is portrayed on vertical.
5th step, owing to drilling well is few, petrofacies are become to be difficult to carry out the research of planar distribution, by depending on corrosion rate, depending on compacting rate and seismic inversion property parameters (p-wave impedance (Ip), S-wave impedance (Is), P-S wave velocity ratio (Vp/Vs)) discovery (Fig. 3): Ip, Is, Vp/Vs have negative correlativing relation with depending on corrosion rate, positive correlation is had with depending on compacting rate, thus known, Areal porosity is more little, rock contact degree is more tight, and velocity of propagation is more fast.
6th step, from geological knowledge, respectively will depending on corrosion rate, do multivariate linear regression depending on compacting rate and Ip, Is, Vp/Vs parameter, regression equation is imported seismic inversion data volume depending on corrosion rate, the inverting attribute of looking compacting rate, in conjunction with the diagenesis intensity criteria for classifying, the distribution range becoming petrofacies is drawn a circle to approve by the superimposed attribute section depending on corrosion rate, depending on compacting rate, finally predicts into the planar distribution of petrofacies.
Depending on corrosion rate=-1.68126e-6Ip+3.83944e-5Is-204.3903488Vp/Vs+103.14788
Depending on compacting rate=4.07672e-6Ip+3.84935e-5Is-244.484517Vp/Vs+80.16936878
7th step, from analytical results, aforesaid method can be accomplished by putting the one-tenth petrofacies quantitatively characterizing to line to face, and the diagenesis facies type becoming petrofacies result of log interpretation and core description matches (Fig. 4), plane prediction one-tenth petrofacies with also can accomplish between the one-tenth lithofacies successions between well good corresponding, weak cementing when accomplished the vertical explanation of one-tenth petrofacies and the plane prediction of quantification the exploratory development of tight gas reservoir to be had important directive significance.
Claims (3)
1. it is an object of the invention to by rock core, well logging and seismic data, set up a kind of for weak cementing, method that deep layer Sandstone Gas Reservoir becomes petrofacies quantitatively characterizing, it is characterised in that also comprise step by the following method and carry out:
(1) by casting body flake quantitative Analysis depending on corrosion rate, look compacting rate;
(2) by core description, ordinary sheet analysis, scanning electron microscopic observation, in conjunction with depending on corrosion rate, determine individual well diagenesis facies type depending on the compacting rate intensity criteria for classifying;
(3) filter out the log parameter to corrosion, compacting effort sensitivity (DT (sound wave), DEN (density), GR (natural gamma), CNCF (neutron), RT (resistivity)) and utilize BP nerual network technique, set up depending on corrosion rate, look compacting rate rain well logging sensitive parameter corresponding relation, obtain into Well log quantitative explanation and the recognition result of petrofacies;
(4) filtering out the seismic inversion attribute sensitive parameter to corrosion, compacting effort sensitivity and in conjunction with seismic inversion data volume, set up depending on corrosion rate, look compacting rate and the corresponding relation of seismic inversion attribute sensitive parameter, the plane obtaining into petrofacies predicts the outcome.
2. a kind of Sandstone Gas Reservoir according to claim 1 becomes petrofacies quantitatively characterizing method, it is characterized in that in described step (3), first to the well logging sensitive parameter filtered out and corresponding depending on corrosion rate, be normalized depending on compacting rate, with remove variable unit between each well unified bring unfavorable, also random deviation and systematic error that logging instrumentation causes is eliminated, ensure that log data can reflect stratum characteristic more accurately, and due to the ununified impact caused of logging suite between each well. Next choose by thin slice quantitative Analysis depending on corrosion rate, look compacting rate and the well logging sensitive parameter of correspondence as train samples, sensitive parameter will be logged well as input neuron, taking depending on corrosion rate, look compacting rate as decision attribute, as output neuron, when training error is less than 0.1%, network is set up and is achieved the goal, and then carries out full well section and become petrofacies Well log quantitative explanation.
3. a kind of Sandstone Gas Reservoir according to claim 1 becomes petrofacies quantitatively characterizing method, it is characterized in that in described step (4), the seismic inversion attribute sensitive parameter filtered out is p-wave impedance (Ip), S-wave impedance (Is), P-S wave velocity ratio (Vp/Vs), corrosion rate will be looked respectively, depending on compacting rate and Ip, Is, Vp/Vs parameter does multivariate linear regression, regression equation is imported seismic inversion data volume and looks corrosion rate, depending on the inverting attribute of compacting rate, in conjunction with the diagenesis intensity criteria for classifying, superimposed look corrosion rate, depending on compacting rate attribute section to become petrofacies distribution range draw a circle to approve, finally predict into the planar distribution of petrofacies.
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| CN108090278A (en) * | 2017-12-15 | 2018-05-29 | 长江大学 | The division methods of clastic reservoir rock Diagenetic Facies |
| CN109031424A (en) * | 2018-08-06 | 2018-12-18 | 中国石油大学(华东) | A method of based on well logging Multiparameter low permeability reservoir Diagenetic Facies |
| CN109884696A (en) * | 2019-03-20 | 2019-06-14 | 中国石油化工股份有限公司 | Sandstone Computational approach of S-wave velocity based on calcite cementation function influence |
| CN112347670A (en) * | 2020-10-26 | 2021-02-09 | 青海大学 | Rockfill material creep parameter prediction method based on neural network response surface |
| CN112578475A (en) * | 2020-11-23 | 2021-03-30 | 中海石油(中国)有限公司 | Compact reservoir dual-dessert identification method based on data mining |
| CN113820754A (en) * | 2021-09-10 | 2021-12-21 | 中国石油大学(华东) | Deep tight sandstone reservoir evaluation method based on artificial intelligence recognition of reservoir lithogenesis |
| CN114441405A (en) * | 2021-12-22 | 2022-05-06 | 中国地质大学（北京） | Quantitative evaluation method for secondary pore increasing amplitude based on compaction and cementation pore reducing trend |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108090278A (en) * | 2017-12-15 | 2018-05-29 | 长江大学 | The division methods of clastic reservoir rock Diagenetic Facies |
| CN108090278B (en) * | 2017-12-15 | 2021-06-01 | 长江大学 | Clastic rock reservoir lithogenic phase dividing method |
| CN109031424A (en) * | 2018-08-06 | 2018-12-18 | 中国石油大学(华东) | A method of based on well logging Multiparameter low permeability reservoir Diagenetic Facies |
| CN109031424B (en) * | 2018-08-06 | 2019-09-03 | 中国石油大学(华东) | A method of based on well logging Multiparameter low permeability reservoir Diagenetic Facies |
| CN109884696A (en) * | 2019-03-20 | 2019-06-14 | 中国石油化工股份有限公司 | Sandstone Computational approach of S-wave velocity based on calcite cementation function influence |
| CN112347670A (en) * | 2020-10-26 | 2021-02-09 | 青海大学 | Rockfill material creep parameter prediction method based on neural network response surface |
| CN112578475A (en) * | 2020-11-23 | 2021-03-30 | 中海石油(中国)有限公司 | Compact reservoir dual-dessert identification method based on data mining |
| CN113820754A (en) * | 2021-09-10 | 2021-12-21 | 中国石油大学(华东) | Deep tight sandstone reservoir evaluation method based on artificial intelligence recognition of reservoir lithogenesis |
| CN113820754B (en) * | 2021-09-10 | 2023-06-06 | 中国石油大学(华东) | Deep tight sandstone reservoir evaluation method based on artificial intelligence identification of reservoir lithofacies |
| CN114441405A (en) * | 2021-12-22 | 2022-05-06 | 中国地质大学（北京） | Quantitative evaluation method for secondary pore increasing amplitude based on compaction and cementation pore reducing trend |
| CN114441405B (en) * | 2021-12-22 | 2023-06-06 | 中国地质大学（北京） | Quantitative evaluation method for secondary hole increasing amplitude based on compaction and cementation hole decreasing trend |
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