Programa de Pós-Graduação em Geofísica - CPGF/IG

URI Permanente desta comunidadehttps://repositorio.ufpa.br/handle/2011/2355

O Programa de Pós-Graduação em Geofísica da UFPA (CPGF) do Instituto de Geociências (IG) da Universidade Federal do Pará (UFPA). Foi o segundo no Brasil a formar recursos humanos em Geofísica em nível de pós-graduação stricto sensu. Criado em 1972, funcionou até 1992 junto com os Cursos de Pós-Graduação em Geoquímica e Geologia.

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Agora exibindo 1 - 19 de 19

Acesso aberto (Open Access)
Cálculo da porosidade: identificação do argilomineral
(Universidade Federal do Pará, 2017-04-20) ALMEIDA, Thales Luiz Pinheiro de; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
In the daily practice of Formation Evaluation it is common the adoption of simplifying hypotheses or premises about the physical properties of the reservoir rock constituent materials to allow the porosity calculation. The knowledge of the physical properties of the clay in the reservoir rock is fundamental for porosity calculation. In this work it is argued that the physical properties of the clay mineral in the reservoir rock are different from the mean physical properties of the nearby shale layers. Geologically, the clay is one of the constituent materials of shale rock and to admit that the physical properties of the shale are equal to the physical properties of the clay in the reservoir rock means to disregard all the other constituents and to assume a sedimentary continuity that due to numerous postpositional processes may not occur. In this work, we apply the angular competitive neural network to the Density- Neutron Plot to show that if a reservoir rock and a shale present in the basin have the same clay, they have the same angular pattern. This methodology is presented with synthetic data and evaluated with actual well logs and core analysis from borehole drilled in the Namorado’s field, in the Campos Basin, Brazil.
Acesso aberto (Open Access)
Cálculo de porosidade com a rede neural competitiva
(Universidade Federal do Pará, 2015-10-26) ROSELLÓN GUZMAN, Laura Yesenia; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Porosity is the petrophysical property that quantifies the fluid volume in the reservoir rock under for subsurface original condition. However, its calculation by the densityneutron method is extremely difficult in non cored borehole by the lack of the knowledge about the matrix physical properties (density and neutron porosity). This work presents a method for enabling the use of density-neutron Method in non cored boreholes, showing a realistic estimate of the matrix physical properties for each reservoir layer, using a angular competitive neural network. For each layer, network training is performed in the density-neutron plot built with the points of this layer and the information about the grain density (matrix density), obtained in the core analysis. This method is presented with synthetic data, which satisfy the petrophysical model and real data from two cored wells in the Namorado field, Campos basin.
Acesso aberto (Open Access)
Caracterização de fraturas em imagens de amplitude acústica utilizando morfologia matemática
(Universidade Federal do Pará, 2013) XAVIER, Aldenize Ruela; GUERRA, Carlos Eduardo; http://lattes.cnpq.br/7633019987920516; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Fractures analysis is of particular interest in the characterization of carbonate reservoir since the fractures are the classic geological setting for stock and produce hydrocarbon in this kinds of reservoirs. Particularly in Brazil is growing the interest in the characterization of carbonate reservoirs, with the recent discoveries in pre-salt. The acoustic imaging tools provide valuable information about the amplitude of the reflected waves in the borehole wall, which can be interpreted to allow the characterization of fractures. However, some problems arise due to the qualitative interpretation of these images that are basically performed with the use of vision and experience of the interpreter. This work presents a methodology that performing the fractures analysis of acoustic images and can be divided into three steps. The first one presents the image modeling, which is used to infer the aspect of the fractures in different geological settings. In the second step, the mathematical morphology is used as an edge detector and performs the fractures identification in the acoustic image. The last step deals with the extraction of geometric attributes of the fractures with the adoption of a four degree polynomial according to the least square criterion. The evaluation of this methodology is performed with synthetic images generated by the presented modeling, which supports the characterization of fractures performed in real images.
Acesso aberto (Open Access)
Correlação de poços com múltiplos perfis através da rede neural multicamadas
(Universidade Federal do Pará, 2001-11-23) AMARAL, Mádio da Silva; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Stratigraphic correlation using well logs is a non-trivial geological activity and subject to endless possibilities of misunderstanding about the geometry or continuity of rock layers, for many reasons, like the geological variability and the ambiguous answers of the log tools. Thus, it is common to utilize a great log suite from the same well, for better comprehension. The stratigraphic correlation is a fundamental tool for a geologist or petroleum geophysist, because from its knowledge it is possible to interpret the hydraulic continuities of the reservoirs and to reconstruct the geological setting environment, which may corroborate for the construction of the reservoir geological model. This work produces an automation of manual activities involved in the stratigraphic correlation, with the use of the various well logs, and a convenient architecture of artificial neural network, trained with the backpropagation algorithm. The stratigraphic correlation, obtained from this method, makes the transport of the geological information possible along the basin and gives the interpreter, a general view of the structural behavior of the oil reservoir. With This methodology was possible the automatic construction of a geological block diagram showing the spatial disposition of a particular shale layer, from the well logs: Gamma Ray (GR), Clay Volume (Vsh), Density (ρb) and the Neutron Porosity (φn), selected in the five wells on the Maracaibo Lake basin, in Venezuela.
Acesso aberto (Open Access)
Deconvolução de perfis de poço através de rede neural recorrente
(Universidade Federal do Pará, 2006-03-05) RUÉLA, Aldenize de Lima; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
For oil industry, the logs analysis is the main information source about the presence and quantification of hydrocarbon in subsurface. However, in two situations the new logging technologies are not economically viable and conventional logging tools must be used: The reevaluation of mature oil fields and evaluation of marginal oil fields. In conventional logs its data acquisition procedure may blur the value of physical property and the vertical limits of a rock layer. We are talking about an old problem in well logging – The paradox between vertical resolution and depth of investigation of a logging tool. Nowadays it is well handling by the high technology of new tools, but this problem persists in conventional old tools, e.g. natural gamma ray log (GR). Here, we present a method to smooth this kind of linear distortion in well logs by an integration of classical well log convolution model with recurrent neural networks. We assume that a well log can be well represented by an in depth convolution operation between the variation of rock physical property (ideal log) and a function that causes the distortion, called as vertical tool response. Thus, we develop an iterative data processing, which acts as a deconvolution operation, composed by three recurrent neural networks. The first one seeks to estimate the vertical tool response; the second one search for the vertical limits definition of each rock layer and the last one is constructed to estimate the actual physical property. To start this process we supply an appropriated first guess of ideal log and vertical tool response. Finally, we show the improvements in vertical resolution and in the physical property evaluation produced by this methodology in synthetic logs and actual well log data from Lagunillas formation, Maracaibo basin, Venezuela.
Acesso aberto (Open Access)
Determinação automática da porosidade e zoneamento de perfis através da rede neural artificial competitiva
(Universidade Federal do Pará, 2000) LIMA, Klédson Tomaso Pereira de; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Two of the most important activities of log interpretation, for the evaluation of hydrocarbon reservoirs are the log zonation and the effective porosity calculation of the rocks crossed by the well. The log zonation is the visual log interpretation for the identification, in depth, of the reservoir layers and its vertical limits, that is to say, it is the formal separation in reservoir rocks and non reservoir rocks (shales). The log zonation procedure is accomplished in a manual way, being been worth of the geologic and geophysical knowledge, and of the experience of the log analyst, in the visual evaluation of the curve patterns (log characteristics) corresponding to each specific rock type. The calculation of the effective porosity (porosity of the rock reservoir corrected by clay effects), combines a visual activity so much in the identification of the representative points of a reservoir rock in the log, as well as the adapted choice of the petrophysics equation, that relates the physical properties of the rock to the porosity. Starting from the knowledge of the porosity, the hydrocarbon volume will be established. This activity, essential for the reservoirs qualification, requests a lot of the knowledge and of the experience of the log analyst, for the effective porosity evaluation. An efficient form of automating these procedures and assistant the log analyst, in these activities, that particularly demand a great expenditure of time, is presented in this dissertation, in the form of a new log, derived of the traditional porosity logs, that presents the log zonation, highlighting the top and base depths of the occurrences of reservoir rocks, and non reservoir rocks, scaled in form of effective porosity, called here, as "zoning effective porosity log". The obtaining of the zoning effective porosity log, is based on the project and execution of several architectures of artificial neural feedforward networks, with not supervised training, and contends a layer of artificial competitive neurons. Projected in way to simulate the behavior of the log analyst, when he uses the neutron-density chart, for the situations of applicability of the shale-sandstone model. The applicability and limitations of this methodology will be appraised on real data, originated from of Lago Maracaibo's basin (Venezuela).
Acesso aberto (Open Access)
Determinação estatística dos contatos fluidos em perfis de poço
(Universidade Federal do Pará, 2001-11-23) FLEXA, Roosevelt Tavares; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
In oil reservoirs, the effect of gravity naturally produces the fluid segregation. Due to capillary forces, there is no defined surface of separation between two differents fluids. However, it is common in petroleum engineering to admit a conventional fluid separation surface, called fluid interface or fluid contact. The depth location of the fluid contacts (oil-water, gas-water and gas-oil) inside the reservoir rocks, without the complete well log interpretation or the accomplishment of the direct procedures involved in formation tests, is a petroleum industry problem. The knowledge of this parameter can be used for well completation procedures and for positioning perforating services in vertical wells. Another application of fluid contact mapping can be to control the drilling bit in directional and horizontal wells, where such information is used to maintain the well axis inside the hydrocarbon zone in order to avoid water production. We present a methodology which can identify and locate fluid contacts, through an application of the multivariate statistical technique called discriminant analysis. For clastics deposits, with sand-shale sequences, discriminant analysis may provide the indication of lithology and the apparent thicknesses of the reservoirs. The well logs applied for the evaluation of this methodology (resistivity (RT), gamma ray (RG), density (ρb), neutronic porosity (ΦN), caliper (Cal) and shaleness (Vcla) are from wells in Lake Maracaibo, Venezuela.
Acesso aberto (Open Access)
Estimativa de porosidade em lâminas petrográficas através da morfologia matemática binária
(Universidade Federal do Pará, 2013-08-02) CASTELO, Fernando Walleson Louzada; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Oil exploration in offshore basins needs for drilling boreholes with high angle and horizontal wells, aimed at optimizing the number of exploration targets hit by a single platform. In these cases, it is technically impossible to carry out the coring operations, which prevents core analysis for direct measurement of porosity. In some situations in formation evaluation the geological knowledge of the area may help when there is low confidence in the porosity values. For the semi-submerged basins, the lateral continuity of geologic layers may allow sampling of outcrops in the immersed part of the basin. In the case of offshore basins, may be collected hand samples in outcrops of analogous formations. A relatively common problem in a petrophysical laboratory is the extraction of plugs adapted to the measuring equipment, directly from rock samples collected from outcrops. On the other hand, for this type of rock sample is trivial to obtain thin sections. The objective of this work is to estimate porosity directly on the petrographic images using the image processing method, known as mathematical morphology, which aims to describe quantitatively the geometric structures (forms) in this image.
Acesso aberto (Open Access)
Estimativa dos perfis de permeabilidade e de porosidade utilizando rede neural artificial
(Universidade Federal do Pará, 2002-11-05) GOMES, Laércio Gouvêa; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
The permeability and the porosity are the two most important petrophysical properties for qualification of oil and gas reservoirs. The porosity is related to the capacity of fluids storage and the permeability, with the production of these fluids. The estimates of the permeability and porosity are of fundamental importance for reservoir engineers and geophysics, once its values can define the completacion or not of an oil well. Its measures are, usually, accomplished in laboratory, through cores of the rock. The porosity log and its relationship with the density log, is very well-known in the well logging, however, it just exist a few qualitative relationships (Kozeny's relation, for instance) between the porosity and the permeability. This work search the establishment of the permeability log and of the porosity log, starting from information of the density log. For so much, we looked for the relationship among the physical property of the rock (density) and the petrophysical properties: permeability and porosity, using as methodology the technique of artificial neural networks with radial base function. To obtaining the permeability and the porosity, the artificial neural network possessing as input the information of the density that facilitates a smaller cost for the acquisition of those important petrophysical information, giving possibility to the well log analysts, to opt or not for the exploration of a studied unit, in addition, it facilitates a more complete vision of the reservoir. The procedures for the estimate of the permeability and of the porosity are addressed for an only formation, but the log interpreters can apply the guideline presented in the program of artificial neural network with radial base function, using the estimate of those properties for another formations, besides of another oil fields. Therefore, is recommended the use of a large data set of the same well in order to make possible the best interpretation.
Acesso aberto (Open Access)
Identificação de fácies em perfis com algoritmo heurístico
(Universidade Federal do Pará, 2015-02-27) ALMEIDA, Thelson Luiz Pinheiro de; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
The development of oil well drilling techniques and the optimization exploitation of various hydrocarbon reservoirs on the same time, an issue has drawn attention from researchers from the oil industry: identification of sedimentary facies in wells not core available. Therefore, this paper proposes the use of a heuristic algorithm based on the behavior of insects, which contributes to the interpretation of M-N Graphic on computational way. Using wells logs and plot your data in graphic M-N, we have aimed to sort of well log points in relation to fixed points, using the creation of groups (clusters) of data that have some similarity or symmetry, based on what we call attractiveness. Using the creation of these groups of points of the well log, in the neighborhood of fixed points through which suffered major attraction, we can do the identification of multiple data families which, in this work, will be taken as layers, depending on which mineral fixed point are closer, can have their main mineral composition identified without the help of the core and thus obtain the sedimentary facies knowledge overpassed by the well.
Acesso aberto (Open Access)
Identificação de fácies em perfis com rede neural direta
(Universidade Federal do Pará, 2015) GOMES, Kivia do Carmo Palheta; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
The application of coring techniques is usually carried out in a limited number of vertical wells drilled in an oil field, causing the rarefaction of facies descriptions and not allowing a realistic characterization of reservoirs. Increased production of hydrocarbons in an oil field is extremely important for the oil industry and deeply dependent on the knowledge of the reserves in accordance with their petrophysical properties, which vary depending on geological facies. A better description of facies may reflect more realistic estimates of hydrocarbon volumes. This dissertation presents an intelligent algorithm capable of producing the transport of geologic information produced by the facies analysis of cores to the non-cored wells in an oil field, through the design of a direct neural network trained to perform a mapping of geological information in terms of the physical properties registered in the well logs. The intelligent algorithm processes the result produced by the neural network through a depth coherence filter to locate the boundaries of the layers along the well trajectory. For all of our cases the intelligent algorithm presented results compatible with the core analysis and independent of the size of the training set.
Acesso aberto (Open Access)
Identificação de fácies em perfis de poço com algoritmo inteligente
(Universidade Federal do Pará, 2014-02-17) SANTOS, Renata de Sena; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Facies identification in an uncored borehole is a classic problem in formation evaluation. In this study, this problem is treated as the extraction of geological information or facies descriptions from a cored borehole in terms of their physical properties registered in well logs and perform their encoding through the parameters L and K calculated from porosity logs, and shaliness calculated using the natural gamma ray log to construct the Vsh-L-K plot. For interpretation is presented an intelligent algorithm based on the competitive generalized angular neural network, built for angular pattern classification or data clustering in ndimensional space that have an approximately ellipsoidal envelope, which are the characteristics of clusters in the Vsh-L-K plot and make your visual interpretation extremely complex. The application of intelligent algorithm is able to identify and classify the layers present in uncored boreholes, in terms of the facies identified in the cored borehole or in terms of its main mineral, when it is absent in the cored borehole. This methodology is presented with synthetic data and well logs from cored boreholes in Namorado oil field, in the Campos Basin, located on the continental shelf of Rio de Janeiro, Brazil.
Acesso aberto (Open Access)
Identificação litológica com affinity propagation
(Universidade Federal do Pará, 2013-03-26) CALDAS, Nayara Safira da Silva; GUERRA, Carlos Eduardo; http://lattes.cnpq.br/7633019987920516; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
This work presents a methodology for solving the problem of extracting geological information, such as identification of lithologies at depth, directly from wireline logs. For this propose, the Vsh-M-N plot is used as the formation evaluation technique for identifying the lithologies in the logged borehole, in terms of the physical properties of the main mineral in each lithology. However, the visual interpretation of this graphic is limited by the large spread of the points in the graph. To evaluate a form of computational interpretation of the Vsh-M-N, the affinity propagation algorithm is used in reason of its characteristic transport of information among similar objects, which enables the interpretation of Vsh-M-N plot considering the physical properties and continuity in the depth. This methodology is presented using synthetic data and well logs from one borehole in the Namorado field. Campos basin. Brazil.
Acesso aberto (Open Access)
Imageamento da porosidade através de perfis geofísicos de poço
(Universidade Federal do Pará, 2004-01-27) MIRANDA, Anna Ilcéa Fischetti; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Porosity images are graphical representations of the lateral distribution of rock porosity estimated from well log data. We present a methodology to produce this geological image entirely independent of interpreter intervention, with an interpretative algorithm approach, which is based on two types of artificial neural networks. The first is based on neural competitive layer and is constructed to perform an automatic interpretation of the classical Pb - ΦN cross-plot, which produces the log zonation and porosity estimation. The second is a feed-forward neural network with radial basis function designed to perform a spatial data integration, which can be divided in two steps. The first refers to well log correlation and the second produces the estimation of lateral porosity distribution. This methodology should aid the interpreter in defining the reservoir geological model, and, perhaps more importantly, it should help him to efficiently develop strategies for oil or gas field development. The results or porosity images are very similar to conventional geological cross-sections, especially in a depositional setting dominated by clastics, where a color map scaled in porosity units illustrates the porosity distribution and the geometric disposition of geological layers along the section. The methodology is applied over actual well log data from the Lagunillas Formation, in the Lake Maracaibo basin, located in western Venezuela.
Acesso aberto (Open Access)
Interpretação do gráfico de Hingle através de rede competitiva angular
(Universidade Federal do Pará, 2007) SILVA, Carolina Barros da; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Water saturation is an important petrophysical property for formation evaluation, defining the final wellbore destination. The Archie’s equation calculates the water saturation for clean formations in function of rock resistivity, from a deep resistivity log and porosity, from one porosity log. The Archie’s equation, still involves the knowledge of formation water resistivity, which requires local determination and appropriated Archie’s coefficients. Hingle plot is traditional method in well logging for water saturation calculus, specially when the water resistivity is unknown. This method promotes a linearization of Archie’s equation from resistivity and porosity logs as the water line in the Hingle plot. The water resistivity is obtained from water line inclination. Independent of logging tools and digital computers development, the log analyst still handles with visual data interpretation and as all visual data interpretation, the Hingle plots interpretation is subject of sharpness errors. The objective of this dissertation is to simulate the visual interpretation of Hingle plot by a angular competitive neural network to mitigate the occurrence of sharpness errors and produces a real time first approach of water saturation, based on angular pattern identification in the raw well logging data.. The evaluation of this methodology is accomplished on synthetic data that satisfies the Archie’s equation and on actual well logging data.
Acesso aberto (Open Access)
Modelamento e correção de descentralização das imagens de tempo de trânsito
(Universidade Federal do Pará, 2003) FISCHETTI, Anna Carmela; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
The imaging tools used to borehole wall features identification have been largely utilized by geologist and oil engineers to identify geological events in a open hole and inspect the casing tube. The acoustic borehole imaging tools generate a transit time image and an acoustic amplitude image that are used to this proposes. However those logs may have a non-realist interpretation, since some tools effect can negatively influence in the images appearance. This paper presents a transit time image model starting from the application of the Coulomb’s approach to the borehole wall rupture submitted to a plane state of tensions which will supply the borehole section that is the geometric form that will be mapped by the acoustic borehole imaging toll. The tool up displacement and the borehole wall imperfections are usually the responsible for the transducer displacement in relation to the borehole axis. This effect may have important responsibility in the acoustic images imperfections. Thus, a computational process of transducer repositioning in the borehole axis position obtains the correction of those images, called decentralization correction. A method of tool decentralization effect correction is presented too based on this model which is proposed based on the plane analytic geometry and in the ray method to the definition of the transit time of the acoustic pulse, with the objective of reconstruct the transit time images achieved by the decentralized tool, that is to say, correct these images becoming as they were achieved by the centralized tool in relation to the borehole axis.
Acesso aberto (Open Access)
Reconhecimento de fáceis em perfis geofísicos de poços com rede neural competitiva
(Universidade Federal do Pará, 2015-02-27) COSTA, Jéssica Lia Santos da; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
The description of a depositional system based on the recognition of sedimentary facies is critical to the oil industry to characterize the petroleum system. In the absence of these facies description by cores or outcrop, we present a methodology based on intelligent algorithm able to identify facies of interest in wireline logs. This methodology uses a competitive neural network to extract geological information from the physical properties mapped in the M-N plot. The competition among neurons identifies the facies of interest, which have been previously identified in a cored borehole in other non-cored boreholes in the same oil field. The purpose of this methodology is to encode and transmit the geological information gained in cored boreholes to non-cored wells and thus achieve the geological interpretation of the facies of interest in an oil field. This methodology has been evaluated with synthetic data and actual wireline logs from two cored boreholes drilled in the Namorado oil field, Campos Basin, Brazil.
Acesso aberto (Open Access)
Solução da equação de Archie com algoritmos inteligentes
(Universidade Federal do Pará, 2011) SILVA, Carolina Barros da; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Archie equation is a historical mark of Formation Evaluation establishing a relationship among the physical properties and the petrophysical properties of reservoir rocks, which makes possible the identification and quantification of hydrocarbon in subsurface. Water saturation is the solution of Archie equation obtained from the measure of formation deep resistivity and porosity estimated. However, the solution of Archie equation is no trivial, in the dependence of previous knowledge of formation water resistivity and Archie exponents (cementation and saturation). This thesis introduces a set new intelligent algorithm to solve Archie equation. A modification of competitive neural network, nominated as bicompetitive neural network produces the log zonation. A new genetic algorithm with evolutionary strategy based in the mushrooms reproduction produces estimates for the matrix density, the matrix transit time and the matrix neutron porosity, which associated to a new rock model, produces realistic porosity estimates considering shale effects. A new model of competitive neural network, nominated as angular competitive neural network is able to accomplish the interpretation of Pickett plot, supplying the information about formation water resistivity and cementation exponent. All results of the methodology hereintroduced are presented using synthetic data and actual wireline logs and core analysis results.
Acesso aberto (Open Access)
Zoneamento de poços através da inferência Fuzzy
(Universidade Federal do Pará, 2015-06-26) RUIZ TAPIA, Alberto José; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Well zoning may be understood as the geological characterization (location and facies description) of each layer crossed by the borehole trajectory. Well zoning is a common activity in conventional core analysis and important for petroleum geology, assisting the construction of stratigraphic column and also for petroleum engineering aiding in the development of the well exploitation plan. This work presents a method for well zoning wells of non cored boreholes, so that the information gained in these wells can contribute to improve the knowledge of sedimentology and oilfield engineering. The method showed here uses the core description for building the knowledge base of a fuzzy inference system, which operates with P parameter (a new combination of density log and sonic log), parameter M (M-N plot) and the natural gamma ray log and the deep resistivity log. Operation of this fuzzy inference system using log data from non cored borehole produces the well zoning of each non cored borehole. This method is presented with synthetic data satisfying the petrophysical model and the Archie Law, and real data of two cored boreholes from the Namorado oilfield, in the Campos Basin.