CN105044770A - Compact glutenite gas reservoir quantificational prediction method - Google Patents

Abstract

The invention relates to the field of energy exploration, and refers to a method for quantitatively predicting tight sandy conglomerate gas reservoirs, comprising the following steps: establishing a geological model of the actual geological characteristics of the research area; Based on the geological model, combined with lithofacies data, logging data and seismic data, the geological body and probability body are calculated by Markov chain Monte Carlo method. The invention obtains drilling shear wave data by establishing a sandy conglomerate gas reservoir geological model and a petrophysical model, and obtains seismic sensitivity parameters of the gas reservoir by analyzing characteristics of the gas reservoir. Using the inversion technology of the present invention to obtain specific geological bodies and probability bodies solves the technical problems of low seismic vertical resolution, overlapping impedance of gas reservoirs and surrounding rocks, and low prediction accuracy of tight sandy conglomerate gas reservoirs; The invention reduces the ambiguity of seismic reservoir prediction.

Description

Quantitative Prediction Method of Tight Sandy Conglomerate Gas Reservoir

technical field

The invention relates to the field of energy exploration, in particular to a quantitative prediction method for tight sandy conglomerate gas reservoirs.

Background technique

As we all know, there is a large amount of energy in underground reserves. However, its distribution is wide and uneven. Before mining, it is necessary to carry out more accurate exploration and research on it. Exploration is mainly carried out by drilling and geophysics. After decades of development, the conventional favorable sandstone reservoirs have been fully developed, and now it is turning to the exploration stage of tight glutenite gas reservoirs. Therefore, only by finely predicting the spatial distribution of tight glutenite gas reservoirs can we accurately judge the exploration potential of this area. Potential, that is, whether the block has abundant energy available for exploitation.

However, the prediction of tight sandy conglomerate gas reservoirs has always been recognized as a difficult problem. The traditional prediction method is to carry out research work on the post-stack seismic data volume. The seismic features are not obvious, and the prediction results are not accurate, so that the later evaluation and mining cannot achieve the expected results. In the case of deep drilling target layers in the study area, the cost of each drilling is quite huge. It brings a lot of economic pressure to scientific research and construction units, and also wastes more manpower and material resources.

There is an urgent need for a technical method that can improve vertical and horizontal resolution and accurately predict tight glutenite gas reservoirs.

Contents of the invention

The quantitative prediction method of tight glutenite gas reservoirs disclosed by the present invention solves the problems of low vertical and horizontal resolution, overlapping impedance of gas reservoirs and surrounding rocks, and low prediction accuracy of tight glutenite gas reservoirs in the prior art. technical problem.

The technical scheme of the present invention is realized in this way: the quantitative prediction method of tight sandy conglomerate gas reservoir comprises the following steps: a, according to the actual geological conditions of the research area, carry out the research on the consistency processing of the logging curve; Based on the study of the prediction method, according to the characteristics of the glutenite reservoir in the study area, the measured parameter data is introduced to establish a petrophysical model in line with the rock mineralogy characteristics of the study area, and the histogram method and the intersection graph method are used on the basis of the longitudinal and shear wave data. Carry out the analysis of sensitive parameters of gas reservoirs; b. Based on the geological data, logging data and seismic data of actual drilling, and on the basis of fine structure interpretation, establish a geological model reflecting the actual geological characteristics of the study area; c. In b. On the basis of step research, pre-stack geostatistical inversion parameters, impedance probability distribution, and variation function are obtained through the analysis of drilling in the work area; d. Pre-stack geostatistical technology: based on pre-stack P-wave data, through Markov chain Monte Carlo algorithm is used to realize the combination of lithofacies data, logging data and seismic data by using the probability distribution function and variogram obtained in step c, and then based on the geological model obtained in step b, Starting from the well point, the interwell complies with the original seismic data, and finally obtains the geological volume and probability volume.

Further, step e is also included: obtaining reservoir physical property parameter volume and probability volume through co-simulation method on the basis of geological volume data, and obtaining reservoir plane prediction result through integration.

Further, step c is specifically as follows: the impedance probability distribution is obtained through the analysis of the relationship between impedance and lithology distribution; the calculation of the variation function comes from well-seismic fitting. The combined results are reliable; while the horizontal east-west and horizontal north-south directions of the variable range are determined by testing the inversion effect of the stable distribution of mudstone or coal seam to determine the variable range parameters.

Further, the specific step a is: according to the actual situation of the study area, carry out the standardization research of the well logging curve, the shear wave forward modeling research and the reservoir sensitive parameter analysis.

The method for quantitatively predicting tight sandy conglomerate gas reservoirs disclosed by the present invention obtains drilling shear wave data by establishing a sandy conglomerate gas reservoir geological model and a petrophysical model, and obtains gas reservoir reservoirs by analyzing the characteristics of the gas reservoirs seismic sensitivity parameters. Using the inversion technology of the present invention to obtain specific geological bodies and probability bodies solves the technical problems of low seismic vertical resolution, overlapping impedance of gas reservoirs and surrounding rocks, and low prediction accuracy of tight sandy conglomerate gas reservoirs; The invention reduces the multiple solutions of seismic reservoir prediction, greatly improves the accuracy of quantitative prediction of tight sandy conglomerate gas reservoirs, thereby avoiding economic losses caused by inaccurate predictions.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

A quantitative prediction method for tight sandy conglomerate gas reservoirs disclosed by the present invention comprises the following steps: a. According to the actual geological conditions in the research area, carry out research on the consistency processing of logging curves; in the research of the Xu-White theoretical model shear wave prediction method Based on the characteristics of glutenite reservoirs in the study area, the measured parameter data was introduced to establish a petrophysical model in line with the rock mineralogy characteristics of the study area. Layer sensitive parameter analysis; b. Based on the existing geological data, logging data and seismic data of actual drilling, on the basis of fine structure interpretation, establish a geological model reflecting the actual geological characteristics of the study area; c. Based on the research in step b. , get pre-stack geostatistical inversion parameters, impedance probability distribution, and variation function through the analysis of wells drilled in the work area; d. Pre-stack geostatistical technology: based on pre-stack P-wave data, through The Monte Carlo algorithm is used to realize the combination of lithofacies data, logging data and seismic data by using the probability distribution function and variation function obtained in step c, and then based on the geological model obtained in step b, starting from the well point , follow the original seismic data between wells, and finally get the geological volume and probability volume.

Further, step e is also included: obtaining reservoir physical property parameter volume and probability volume through co-simulation method on the basis of geological volume data, and obtaining reservoir plane prediction result through integration.

Further, step c is specifically as follows: the impedance probability distribution is obtained through the analysis of the relationship between impedance and lithology distribution; the calculation of the variation function comes from well-seismic fitting. The combined results are reliable; while the horizontal east-west and horizontal north-south directions of the variable range are determined by testing the inversion effect of the stable distribution of mudstone or coal seam to determine the variable range parameters.

Further, the specific step a is: according to the actual situation of the study area, carry out the standardization research of the well logging curve, the shear wave forward modeling research and the reservoir sensitive parameter analysis.

The above-mentioned prediction method provides favorable conditions for later mining, making the mining process smoother, and the amount of energy obtained in the later actual drilling achieves the expected effect, and at the same time avoids the inaccurate prediction of tight sandy conglomerate gas reservoirs. The resulting empty drilling and a small amount of energy obtained after drilling save the production cost of scientific research and construction units, and provide important and accurate information for energy exploration and exploitation.

Specifically, the consistent processing of well logging data described in step b, that is, the standardization processing, is to provide complete logging curves consistent among multiple wells for logging interpretation and petrophysical modeling. Its main purpose is to eliminate the measurement deviation of the logging curve caused by the logging and drilling environment, and to correct the systematic differences in the measurement results caused by different instruments, different measurement environments and borehole conditions among multiple wells. On the basis of consistent processing, combined with the interpretation results of parameters such as shale content, quartz content, calcium content, porosity, and saturation obtained from multi-well formation evaluation processing, a petrophysical model for tight sandy conglomerate gas reservoirs was established. By fine-tuning the parameters of the skeleton points, the model data and the measured data can reach a high correlation, and then determine the optimized petrophysical model and skeleton parameter points that can be used in this study area, so as to establish a petrophysical model suitable for this study area. The establishment of the geological model is mainly based on the standardization of well logging data, combined with the results of rock physics forward modeling, to create a geological framework model based on the layers and the relationship between the layers that have been finely interpreted by the earthquake. In the construction of the frame model, the input layer The data is smoothed, and the smoothed layer data are carefully checked and adjusted on the seismic data volume, and then interpolated within the entire seismic grid range. After the above special processing, a reasonable geological framework model is finally constructed . Within the framework of the geological model, the inverse distance interpolation algorithm is used to guide the interpolation and extrapolation of the low-frequency trend model in space, and finally obtain a solid model that conforms to the geological characteristics of the study area.

Inversion parameter analysis mainly refers to probability distribution function (probability density function) and variation function (Variation function). The probability distribution function describes the probability distribution of rock physical parameters corresponding to a specific lithology in space. For sequential Gaussian simulation, the data must obey the Gaussian distribution. Therefore, the data should be analyzed before the simulation. convert. The variogram describes the structure and characteristic scale of horizontal and vertical geological features, and is a basic tool in geostatistics to describe the spatial structure and randomness of regionalized variables. In geostatistical inversion, the vertical variogram is obtained from the well data. The horizontal variogram is often limited by the drilling density and should not be obtained directly from the analysis of the well. At present, the commonly used methods mainly include: ① According to Based on the established geological information database, combined with the sedimentary environment characteristics of the study area and seismic attribute analysis, qualitatively determine the variation range (variogram) of sedimentary bodies in different sedimentary environments; ②quantitatively determine the variables at the level change in direction.

The realization of quantitative reservoir prediction technology needs to classify lithology into predictable types under the analysis of actual geological conditions and combined with the results of well logging and formation evaluation. Among them, there is a good statistical relationship between the ratio of P-wave velocity and shale content of glutenite and mudstone, and the impedance of conglomerate and glutenite has a good statistical relationship, so that the compression-wave impedance and the ratio of P-wave velocity to S-wave can be used to identify And simulate the above lithology. In order to reduce the statistical fluctuation error caused by a single simulation, multiple lithology simulations were carried out, and then statistical calculations were performed on the lithology probability volumes and attribute probability volumes realized multiple times to obtain the impedance volume, P-to-S wave velocity ratio volume and Maximum likelihood lithology. Based on the results of high-resolution lithology simulation and the rock elastic parameter body, the porosity of sandstone can be co-simulated through the mathematical method of multi-axis Gaussian co-simulation to obtain an accurate porosity body.

In the following, the present invention takes the lower part of the Xu4 member of the Xinchang gas field in the Western Sichuan Depression of the Sichuan Basin as an example to carry out the quantitative prediction of tight sandy conglomerate gas reservoirs to illustrate the application of the method:

Geological overview of the Xinchang gas field in the Sichuan Basin: The Xinchang gas field is located on the Xiaoquan-Fenggu NE large-scale uplift belt in the middle section of the Western Sichuan Depression in the Sichuan Basin. The uplift belt is located between the Pengzhou-Deyang syncline and the Zitong syncline. The ancient and modern complex large-scale uplift belt has experienced multiple tectonic movements since the Triassic. A series of NEE-trending and SN-trending local structures are distributed on the uplift belt, namely Xinchang, Hexingchang, Fenggu and other structures. The Xinchang gas field is located at the western end of the uplift belt. The Xinchang structure is NEE-trending as a whole, separated from the Hexingchang structure in the east by a low saddle, and connected with the Yazihe structure in the west by a low saddle. The Xinchangxu 4th structure is a nose-shaped anticline composed of several local high points such as Xiaoquan high point, Xinchang high point, and Luojiang high point. The Duanxinchang high point is separated from the Luojiang high point by a SN-trending fault. The structure is high in the west and low in the east, steep in the south and gentle in the north.

The lower Xu 4th member of the Xinchang gas field is mainly a braided river delta depositional environment, with rapid depositional facies, conglomerate in the north, gradually transitioning to glutenite and sandstone in the south, and the lithological structure is extremely complex. From the analysis of drilling and coring data, it can be concluded that the distribution of porosity values in the lower Xu 4th member is relatively scattered, with an average porosity of 5.2%, a median of 5.01%, mainly distributed between 5-6%, and an average reservoir permeability. The value is 0.67md, the median value is 0.077md, and it is mainly distributed in the three intervals of 0.08-0.16md, 0.04-0.08md and 0.16-0.32md. The reservoir pore-permeability relationship is poor, the heterogeneity is strong, and the reservoir type is There are three kinds of pore type, fracture-pore type and fracture type. The lithology of the reservoir is sandy conglomerate. In addition to mudstone, there are also conglomerate widely distributed in the north. The conglomerate is mainly calcareous cemented, with an average porosity of less than 3%. , is a non-reservoir. Generally speaking, the reservoirs in the Lower Xu 4th Member are characterized by complex lithology, rapid lithofacies changes, poor physical properties, strong heterogeneity, and various reservoir types. The distribution of gas reservoirs is mainly controlled by sedimentary facies belts, and the relatively high porosity Glutenite reservoirs are a major target for earthquake prediction.

Seismic prospecting overview: From 2004 to 2005, the 3D3C seismic survey in the Xinchang area was completed with a full coverage area of 156.6km ² and a primary coverage area of 529.0km ² ; from 2008 to 2009, the Hexingchang-Gaomiaozi 3D3C seismic survey in the east of the Xinchang gas field was completed The full coverage area of exploration is 129.5625km ² , and the primary coverage area is 566.8025km ² ; from 2009 to 2010, the 3D3C seismic exploration in Xiaoquan west of Xinchang gas field has a full coverage area of 129.5625km ² , and the primary coverage area is 566.8025km ² .

Logging consistency processing and petrophysical modeling: the standardized processing of logging data is to provide complete logging curves that are consistent among multiple wells for logging interpretation and petrophysical modeling. Its main purpose is to eliminate the measurement deviation of the logging curve caused by the logging and drilling environment, and to correct the systematic differences in the measurement results caused by different instruments, different measurement environments and borehole conditions among multiple wells. On the basis of consistent processing, combined with the interpretation results of parameters such as shale content, quartz content, calcium content, porosity, and saturation obtained from multi-well formation evaluation processing, a petrophysical model for tight sandy conglomerate gas reservoirs was established. By fine-tuning the parameters of the skeleton points, the model data and the measured data can reach a high correlation, and then determine the optimized petrophysical model and skeleton parameter points that can be used in this study area, so as to establish a petrophysical model suitable for this study area, thereby We are producing reliable shear wave data. In the process of analyzing the lower subsection, 13 wells were first selected as the intersection of P-wave impedance and P-to-S wave velocity ratio. The GR value between 28 and 70 is the sandy conglomerate gas reservoir, corresponding to imp8e+06~1.4e+07 , vp/vs: 1.6～2.2. The GR value between 70 and 120 is mudstone and non-reservoir, corresponding to imp8e+06~1.4e+07, vp/vs: 1.6~2.2. At the same time, according to the lithological interpretation results of mud logging, the P-wave impedance and the P-to-S wave velocity ratio of the lower subsection are combined, and the mudstone is characterized by low impedance and high VP/VS, and the sandy conglomerate is characterized by medium-high impedance and medium-low VP/VS. Conglomerate is characterized by high impedance and medium-low VP/VS; when the reservoir contains oil, gas and water layers, it is characterized by low impedance and relatively low VP/VS; when the reservoir is dry, it is characterized by relatively high impedance and relatively High VP/VS features;

According to the comparative analysis of the forward modeling results and lithology division, the P-to-S wave velocity ratio has a good correlation with the shale content, indicating that the P-to-S wave velocity ratio is an important rock elastic parameter for lithology division. According to the lithological statistics of Xusi strata in Xinchang, it can be seen from the statistical results of several commonly used elastic parameters: the P-wave impedance of the glutenite in Xusi is slightly higher than that of mudstone in general, and the P-wave impedance of conglomerate is the highest; , the superposition of glutenite and mudstone is very serious, and the P-wave impedance of tight conglomerate is relatively higher than that of glutenite and mudstone, indicating that P-wave impedance can effectively distinguish tight conglomerate and glutenite. The difference in shear wave velocity between glutenite and mudstone is larger than that of longitudinal wave velocity, but the superposition of shear wave impedance is still serious. In terms of the ratio of longitudinal and transverse wave velocity, it is relatively easy to identify glutenite and mudstone, indicating that the ratio of longitudinal and transverse wave velocity can be Effectively distinguish between sandy conglomerate and mudstone. Therefore, it is difficult to distinguish reservoirs from non-reservoirs by using only P-wave impedance, that is, post-stack inversion is difficult to predict Xusi sandy conglomerate reservoirs in this area, and the pre-stack method technology of the present invention must be used to realize effective prediction of reservoirs .

Geological model establishment: On the basis of standardization of well logging data, combined with rock physics forward modeling results, a geological framework model is created with fine seismic interpretation of horizons and the relationship between horizons, and a reasonable interpolation algorithm is used to guide Interpolation and extrapolation of the low-frequency trend model in space, the model selected in the project can basically reflect the sequence relationship of the strata in this area. During the establishment of the geological model, six interpretation horizons were selected: T41, T53, T52, T51, T511, and T6.

The logging data of the whole work area is comprehensively used for model calibration, and the input layer data is smoothed according to the actual needs of seismic hydrocarbon detection, and then interpolation and smoothing of interpreted layers are performed in the entire seismic grid range. After careful inspection and adjustment of the smoothed layer data on the seismic data volume, the geological layers of T53 and below all use the "top-bottom parallel" relationship to reflect the inheritance relationship of geological micro-layers. After the above processing, the constructed A suitable geological framework model can be used to describe the stratigraphic structure and stratigraphic characteristics within the corresponding geological horizon. In subsequent work, the log data were interpolated along the geological horizon to provide a solid model on which to operate.

Inversion parameter analysis: The inversion parameters of pre-stack geostatistics can be obtained by analyzing the wells drilled in the work area. The analysis of 13 wells with good logging curves in the Xinchang area shows that the impedance probability distribution strictly follows the Gaussian distribution, with a median value of 1.14952×107(kg/cm ³ *m/s) and a variance of 1.478611(kg/cm ³ * m/s) ² ; the calculation of the variogram comes from the well-seismic fitting. In the fitting process, it is necessary to determine the ranges in three directions, namely the longitudinal direction, the horizontal east-west direction, and the horizontal north-south direction. Since the longitudinal sample There are many points, and the fitted vertical range is relatively solid. Through the comparison of Gaussian and exponential fitting, the exponential fitting in this area is better, and the vertical range is 6m; while the other two horizontal ranges are often Due to the lack of horizontal sampling data points, the study is determined by the understanding of geological deposition laws, and the variable range parameters can be determined by testing the inversion results of stable distribution of mudstone or coal seams.

Realization of quantitative prediction: According to the actual geological conditions of the Xu4 Member reservoir, which is characterized by thin vertical development and strong lateral heterogeneity, this study uses pre-stack geostatistical inversion technology to map the Xu4 Member of Xinchang The lithology is divided into three types: conglomerate, glutenite and mudstone. Among them, there is a good statistical relationship between the ratio of P-wave velocity and shale content of glutenite and mudstone, and the impedance of conglomerate and glutenite has a good statistical relationship, so that the compression-wave impedance and the ratio of P-wave velocity to S-wave can be used to identify And simulate the above lithology. In order to reduce the statistical fluctuation error caused by a single simulation, 20 lithology simulations were carried out, and then the lithology probability volume and attribute probability volume realized in 20 times were statistically calculated to obtain the impedance volume, P/S wave velocity ratio volume and Maximum likelihood lithology. From the lithological profile, it can be seen that various rock properties are displayed intuitively, and the lateral changes of sand bodies are relatively clear and highly consistent with the actual drilling results.

Based on the results of high-resolution lithology simulation and rock elastic parameter body, the porosity of sandstone can be co-simulated by the mathematical method of multi-axis Gaussian co-simulation. According to the logging analysis results, high-quality reservoirs are mainly located in low-wave impedance areas, and there is a high correlation coefficient between reservoir porosity and P-wave impedance, which also means that the co-simulation method can obtain accurate porosity bodies.

The pre-stack geostatistical inversion can obtain multiple lithology realizations that are completely consistent with the same well and spatially consistent with the deterministic inversion results. Finally, the final extreme pole is obtained through comprehensive calculation through multiple geostatistical lithology probability realizations. Large likelihood lithology body. Through the fine interpretation of each lithology interface on the inverted lithology body, the cumulative integration along the top and bottom of each sand formation obtains the plane distribution of the reservoir thickness of each description unit in the Xu 4 Member. Taking the TX ₄₉ sand formation as an example, ^on the basis of inversion of lithological bodies, given the threshold value of porosity, the plane distribution of reservoir thickness with different porosities can be obtained, and the average porosity greater than a specific porosity value can also be obtained Plane distribution characteristics, and then delineate high-quality reservoirs and identify sweet spots. Therefore, these results obtained through geostatistical inversion can not only be directly used in the compilation of sedimentary facies map, reservoir description, and gas reservoir description in line with reserve calculation, but also provide a basis for the deployment of exploration and development wells.

This method effectively integrates geological, logging, and seismic data. It organically combines seismic lateral resolution and logging vertical resolution. The difficult problem of reservoir prediction has been solved, and important progress has been made in the prediction of subtle reservoirs of tight sandy conglomerate gas reservoirs. With the improvement of the method and technological progress, the technology has a broader development space and is expected to play an increasingly important role in gas reservoir description.

The method for quantitatively predicting tight sandy conglomerate gas reservoirs disclosed by the present invention obtains drilling shear wave data by establishing a sandy conglomerate gas reservoir geological model and a petrophysical model, and obtains the seismic data of the reservoir by analyzing the characteristics of the reservoir. Sensitive parameters. Using the inversion technology of the present invention to obtain specific geological bodies and probability bodies solves the technical problems of low vertical resolution, overlapping impedance of gas reservoirs and surrounding rocks, and low prediction accuracy of tight sandy conglomerate gas reservoirs; The invention reduces the ambiguity of seismic reservoir prediction, greatly improves the accuracy of quantitative prediction of tight sandy conglomerate gas reservoirs, thereby avoiding economic losses caused by inaccurate predictions.

Of course, without departing from the spirit and essence of the present invention, those skilled in the art should be able to make various corresponding changes and deformations according to the present invention, but these corresponding changes and deformations should all belong to the attached scope of the present invention. The scope of the claims.

Claims (4)

1. tight sandy gravel Gas Reservoir quantitative forecasting technique, is characterized in that, comprises the following steps:

A, according to the actual geological condition in study area, carry out logging trace consistency treatment research; On the basis of Xu-White theoretical model shear wave Study on Forecasting Method, for the feature of study area sandy gravel materials, introduce actual measurement supplemental characteristic, set up and meet the petrophysical model of study area petromineralogy feature, the basis of ripple data in length and breadth uses histogram method and plot method to carry out the analysis of Gas Reservoir sensitive parameter;

B, geologic information, log data and geological data according to existing actual well drilled, on the basis of fine structural interpretation, set up the geologic model of the actual geologic feature in image study district;

C, on b step Research foundation, by obtaining prestack geostatistical inversion parameter, impedance probability distribution, variogram to the analysis of drilling well in work area;

D, prestack geostatistical techniques: with prestack P ripple data for data basis, realized by Markov chain Monte-Carlo algorithm, utilize the probability distribution function that obtains in step c and variogram that petrofacies data, well-log information and geological data are combined, based on the geologic model obtained in step b again, from well point, defer to original earthquake data between well, finally obtain geologic body and probability volume.

2. tight sandy gravel Gas Reservoir quantitative forecasting technique according to claim 1, it is characterized in that: also comprise step e: on the basis of geologic body data, obtain reservoir physical parameter body and probability volume by the method for association's simulation, and obtain reservoir planar prediction result by integration.

3. tight sandy gravel Gas Reservoir quantitative forecasting technique according to claim 1 and 2, is characterized in that: step c particularly: impedance probability distribution is obtained by impedance and lithology distributions relationship analysis; Asking for of variogram comes from well shake matching, and in the process of matching, longitudinal sample point is more, and fitting result is reliable; And the range of two horizontal directions in north-south of the East and West direction of transverse direction and transverse direction is by stable testing distribution mud stone or coal seam efficiency of inverse process determination range parameter.

4. tight sandy gravel Gas Reservoir quantitative forecasting technique according to claim 3, is characterized in that: described step a particularly: according to study area actual conditions carry out logging trace Standardization Research, shear wave just drilling research and the analysis of reservoir sensitive parameter.