INTEGRATED RESERVOIR CHARACTERIZATION AND MODELING

Transcription

1 INTEGRATED RESERVOIR CHARACTERIZATION AND MODELING Mickaele Le Ravalec Brigitte Doligez Olivier Lerat ISBN: EAN: Introduction Book DOI: /ifpen/ Introduction DOI:

2 We briefly recap in the following pages what a reservoir model is, which kinds of data can be considered, and how data integration can be performed. The main point is to emphasize the need for implementing integrated workflows. 1

3 As soon as we are interested in underground geological objects, we have to face a major difficulty: the measurements performed to better characterize these objects are very expensive. As such, they are rather limited. In such conditions, numerical simulation is a useful and complementary tool. We then refer to computer models to represent the objects under consideration. These models are given as input to flow simulators, which provide numerical responses. This helps understand fluid flows. According to Wikipedia: - reservoir modeling involves the construction of a computer model of a petroleum reservoir, for the purposes of improving estimation of reserves and making decisions regarding the development of the field, and - reservoir characterization refers to reservoir modeling activities up to the point when a simulation model is ready to simulate the flow of fluids. 2

4 Therefore, a central goal in reservoir characterization and modeling is to build reliable reservoir models, which help decision makers optimize field development. The more data-consistent the model, the sounder the predictions. Thus, the key point is to integrate and reconcile all available data - geological, geophysical and production data - into reservoir models. Creating asset teams with geologists, geophysicists and reservoir engineers is a first step towards integration, but it is not enough. There is a clear need: 1) for integrated modeling workflows focusing on a single objet, i.e., the reservoir model, and 2) for advanced techniques to adjust the reservoir model at any level of the workflows and strengthen its consistency with data. 3

5 Oil and gas reservoirs are geological formations with fluids within the pore space. These formations lie deep underground and have very complex configurations and properties. The pore space comprises the spaces that are unoccupied by the solid rock material: e.g., spaces between grains and fractures. Usually, formations are heterogeneous, compressible, while the fluid is multiphase, multicomponent, and with interactions between phases and components. A reservoir is mathematically an infinite-dimensional system, i.e., an infinite number of parameters is required to describe a reservoir fully, because both the rock formation and the fluid in the pore space are continuous and have very complicated and variable properties. 4

6 The physical system that describes reservoirs is governed by mainly three fundamental laws and all mathematical techniques are simply an application of these fundamental equations. - Mass conservation law: the total mass is conservative, i.e., the difference between the mass inflow and outflow in a cube equals the sum of mass accumulation. - Darcy s law: derived empirically, this law indicates a linear relationship between the fluid velocity relative to the solid rock and the pressure head gradient. Its theoretical basis was provided by, e.g., Whitaker (1966). - Equation Of State (EOS): this is an algebraic expression to represent PVT properties of phases. Numerous EOS have been proposed to represent the phase behavior of pure substances and mixtures in the gas and liquid states since Van der Waals introduced his expression in These equations were generally developed for pure fluids and then extended to mixtures through the use of mixing rules. Other equations are also considered: - Energy conservation law: the energy is conservative (to be considered when reservoirs are produced using for instance steam injection). - Relative permeability and capillary pressure relationships: these are dynamic relationships in multiphase flows. The relative permeability kr of a phase is a function of water saturation Sw while water saturation is related to capillary pressure Pc. We combine these fundamental laws and the initial and boundary conditions so that the reservoir is represented as a mathematical model. 5

7 As solving the continuous problem is not straightforward, it is approximated by a discrete system, i.e., an array of discrete cells, delineated by a grid, which may be regular or irregular. This array is usually three dimensional, even though 1D and 2D models are sometimes used. It often comprises several millions of grid blocks. This numerical object is the reservoir model. In short, a 3D reservoir model consists of the following elements: 1) upper and lower boundaries that correspond to the top and base horizons (they are determined from logs and seismic interpretation), 2) faults that also contribute to the definition of the structural model, 3) facies and petrophysical properties such as porosity, permeability, saturations, net-to-gross (the value of each property is implicitly deemed to apply uniformly throughout the volume of the cell), and 4) properties describing the fluids in place with kr-pc curves, a PVT model describing formation volume factors and solution gas ratios, Water-Oil Contact (WOC) and Gas-Oil Contact (GOC), coefficients describing the activity of aquifers... All of these elements are interconnected. They are highly dependent on one another. For instance, water saturation can be a function of the height above contact and the height itself depends on the position of the reservoir, the contacts, porosity and permeability. The net-to-gross ratio, denoted N/G or NTG, is the proportion of rock formed by reservoir rock (range is 0 to 1). 6

8 We focus on the identification of petrophysical properties because - they are related, directly on indirectly, to all type of data - geological, geophysical and production data; - they (especially permeability k) are often the main properties influencing fluid flows. The spatial distributions of petrophysical properties are very difficult to characterize for at least the reasons listed below. First, petrophysical properties can be very heterogeneous (Dutton et al., 2003; Eaton, 2006), meaning that they can vary a lot with space. Second, there is a deep lack of quantitative data. Except for seismic, most reservoir data are determined at well points. These points account for less than 1% of the volume of the whole reservoir even for fully developed and brown fields. Third, measurements are subject to errors. This contributes to induce high uncertainty. 7

9 Referring to Wikipedia, homogeneity and heterogeneity are defined as follows. - Homogeneity is the state of being homogeneous. Pertaining to the sciences, it is a substance where all the constituents are of the same nature; consisting of similar parts, or of elements of the like nature. - Heterogeneity is the state of being heterogeneous. It is the nature of opposition, or contrariety of qualities. It is diverse in kind or nature; composed of diverse parts, or resulting from differing causes. In general, a heterogeneous entity is composed of dissimilar parts, hence the constituents are of a different kind. In short, a given property is homogeneous when it is the same everywhere. When it varies with space, it is said to be heterogeneous. Reservoirs are inherently heterogeneous because of the variety and the complexity of geological formation processes. This results in spatial variations in porosity, permeability, saturation, facies distribution, fault and fracture distributions, orientations... The following slides point out the occurrence of heterogeneities at all scales, focusing mainly on 4 of them (Schulze-Riegert and Ghedan, 2007): micro, macro, mega and giga scales. 8

10 Reservoir heterogeneity exists at multiple scales and is related to complex and intricate geological formation processes such as erosion, transport and deposition of sediments. Thus, channels, lobes, bars or meanders can be observed at large scale. Heterogeneity can be also evidenced inside these geological objects. For instance, lateral accretion and meter-scale cross-bedding are often observed in channels. Besides, changes in the nature of contacts, grain size or fracture density are commonly detected at the much smaller scale of cores. Reservoir heterogeneity produces variations in petrophysical properties. For instance, permeability of the Culebra Dolomite aquifer, approximately 450 m above the repository horizon of the Waste Isolation Pilot Plant in southeastern New Mexico, USA, was shown to vary by five orders of magnitude (Holt, 1997). On the other hand, Henriette et al. (1989) made detailed permeability measurements of porous blocks of sandstone and limestone measuring cm 3. Each block was cut into three hundred small plugs and permeability was measured for each of them. Permeability variations were shown to approximately span two orders of magnitude. 9

11 Microscopic heterogeneities occur at the level of pores and grains. They are evidenced from laboratory measurements and can be expressed in terms of sizes of pores and pore throats, shapes and sizes of grains, openings of throats, types of minerals, roughness of pores, etc. These heterogeneities have to be related to formation processes: deposition, subsequent deformation phenomena with compaction, cementation, and dissolution. These microscopic heterogeneities tend to create preferential flow paths at the pore scale: the displacing fluids flow while some residual or trapped oil is left behind. 10

12 Macroscopic heterogeneities are detected at the level of cores. Again, they are identified from laboratory measurements. Experiments are performed to estimate porosity, permeability, saturation, capillary pressure, wettability, etc. They can vary from one core to another one even though extracted from a very close location. The laboratory experiments are usually used to calibrate the logs and well tests. They are provided as inputs to the flow simulators. Macroscopic heterogeneities influence the shape of the displacing fluid front. They impact the sweep efficiency and as a consequence the amount of bypassed oil. 11

13 Megascopic heterogeneities correspond to the interwell spacing scale. They can be detected from well test analysis, log correlations, or from the comparison of reservoir simulations with production data. Megascopic heterogeneities include lateral discontinuities of individual strata, pinchouts, fluid contacts, trends, reservoir compartmentalization They basically impact fluid flow the same way as macroscopic heterogeneities, but at a larger scale. In short, they can speed up the displacing fluids to the producers leaving behind large quantities of bypassed oil. 12

14 Last, the gigascopic heterogeneities occur at the basin scale. This is the delineation reservoir scale. In other words, gigascopic heterogeneities correspond to variations in reservoir architecture. They can be caused by the original depositional phenomena or subsequent structural deformations and modifications due to the tectonic activity. Understanding how they are spatially distributed is essential. Some of the oil saturated areas may remain uncontacted if gigascopic heterogeneities are poorly described. 13

15 As previously emphasized, reservoir heterogeneities may affect fluid flow at any scale. They are mainly related to deposition processes, diagenesis and/or the structural imprint on the reservoir. The sedimentary structures (figure 1), facies associations, depositional environments and geometry of sedimentary bodies produce heterogeneities at various scales (Eschard and Doligez, 1993). Their spatial distributions are controlled by the stacking pattern of genetic units (figure 2) - sequences defined at the reservoir unit scale. Besides, the mechanical or chemical transformations that affect sediments after deposition (i.e., diagenesis) are another source of heterogeneity, which can be superimposed to the previous ones (figure 3). As an example, dolomitization (white zone in figure 3) can affect the reservoir, modifying its initial properties. This type of diagenesis can be associated to faults. Faults can disconnect or connect distinct regions of the reservoir (figure 4). They have a major impact on reservoir production. When sealed, they act as barriers to flow. On the other hand, they can also contribute to fluid flow even more than the rock matrix all around. Methodologies have been proposed (Van de Graff et al., 1992) to evaluate how much heterogeneities impact fluid flow referring to the analysis of parameters describing facies reservoir properties (mainly N/G, porosity and permeability) as well as parameters describing the geometric features of the reservoir (sand bodies and faults). 14

16 Reservoir models are inputs to flow simulators. They can be used to evaluate fluid displacements: the reliability of the simulated responses obviously depends on the reliability of the models themselves. A model is considered as reliable provided it respects the available data. Data are split into two main groups: static and dynamic data. Static data do not vary with time. They include: - measurements on core samples extracted from wells: they provide very high-resolution information; - downhole logging tools: they are used to describe the electrofacies and the petrophysical variations along wells. Bedding, fracture features, faults and stratigraphic features can be identified and quantified by borehole imaging tools and core analysis. Such data are said direct as they provide values directly characterizing the studied property. 15

17 However, such direct measurements of geological and petrophysical properties are very sparse and sample only a small reservoir volume. They are supplemented by indirect measurements: 3-D seismic. 3-D seismic data are indirect as they do not directly characterize the target property. Due to its usual low resolution, 3-D seismic is routinely used for defining only the structural model. However, unlike laboratory and log data, 3-D seismic provides information over large areas, which makes it an invaluable candidate for better characterizing geological and petrophysical properties. In the case of high resolution seismic, we can use inverted seismic data instead of focusing on raw seismic data (Doligez et al., 2002; Dubrule, 2003; Lerat et al., 2007). Inverted seismic data basically comprise velocities and impedances. Seismic inversion refers to the inverse modeling of reservoir properties from raw seismic data. Inverted seismic data can help capture reservoir property variability between wells. 16

18 The second group of data includes dynamic data, i.e., data which vary with time because they depend on fluid flows. They mainly comprise production data, i.e., data measured at wells such as bottom hole pressures, oil production rates, gas-oil ratios, tracer concentrations, etc. 17

19 Since the late nineties, dynamic data also consist of data derived from 3-D seismic surveys repeated at successive times, that is 4-D seismic data (Eastwood et al., 1994; Benson and Davis, 2000; Arts et al., 2002; Behrens et al., 2002; Guderian et al., 2003; Roggero et al., 2012). Ideally, a base 3-D seismic survey is acquired before starting production. Then, monitor surveys are successively acquired after a few years of production. The differences between the successive seismic responses are caused by fluid movements, pressure changes, temperature changes, fluid property or compositional changes, or rock changes. They help monitor fluid fronts and pressure domains between and beyond wells. 18

20 Each of the data types mentioned above is associated to its own scale of measurement, its own level of precision, and a given method of measurement with its own physical principles. This makes data integration even more challenging. 19

21 Data help understand reservoir heterogeneity. Thus, well log and core data are used to identify facies as well as petrophysical properties (e.g., porosity, permeability...). In addition, they make it possible to assess diagenesis. On the other hand, well tests and production data provide information about permeability and connectivity in between wells. They contribute to improve our understanding of fluid flows and sweep efficiency. Last, seismic data are used to infer the structure of reservoirs with mainly horizons (top and base) and faults. They permit to determine the boundaries of the major stratigraphic units, which impacts fluid flows and sweep efficiency. In case of high resolution data, they may also help capture reservoir heterogeneity. 20

22 The distinct measurements are used to identify and characterize different groups or types of rocks. In geology, a sedimentary facies is a distinctive rock, which forms under certain conditions of sedimentation, reflecting a particular process or environment (Reading, 1996). It is widely used in connection with sedimentary rocks, but it is not restricted to them. The term lithofacies is used to refer to a facies characterized by particular lithologic features (e.g., grain size, mineralogy, bedding). A lithofacies may be a single bed a few millimeters thick or a group of beds hundreds of meters thick. A lithotype is a lithofacies or group of lithofacies defined on the basis of certain selected petrophysical properties. Log types or electrofacies are also identified from the classification of wireline log data. In this case, a classe is associated to a group of data with similar electrical / radioactive / acoustic /... characters. To be meaningful, these classes have to be calibrated to core information: facies, plug data, etc. It is worth mentioning that they may depend on fluid properties. A rock type is lithotype or log type defined from a fluid-flow point of view. It is an interval of rock with unique pore geometry, determined mineralogical composition and is related to certain specific fluid flow features (saturation, relative permeability, capillary pressure, SCAL - Special Core Analysis - measurements...). 21

23 As previously stated, there are not enough data and there are also uncertainties in the available data due to errors in the measurements or errors in interpretation. In the case of geophysical data, this entails uncertainties in the determination of reservoir structure (locations of horizons, of faults), hence in the volume of hydrocarbon initially in place (HCIIP) and eventually in production forecasts. Similarly, the uncertainties in geological data involve uncertainties in sedimentary and petrophysical models. Again, this means uncertainties in the volume of hydrocarbon initially in place and in production forecasts. On the other hand, the uncertainties in dynamic reservoir data strongly impact the flow simulation results, hence the accuracy of the production forecasts. Likewise, the uncertainties in fluid data can significantly influence the production forecasts, more especially in Enhanced Oil Recovery (EOR) contexts. The various sources of uncertainties (Vincent et al., 1999; Corre et al., 2000; Schulze- Riegert and Ghedan, 2007) are briefly recapped hereafter. 22

24 There are many sources of uncertainties in seismic data that can be identified during the acquisition, processing and interpretation phases. They are listed above. For illustration purposes, various experiments were performed to reveal and quantify uncertainty in seismic interpretation (Rankey and Mitchell, 2003; Bond et al., 2007). They showed that you also have to account for conceptual uncertainty. This actually explains why you may have a large range of interpretations even though based upon a single data set. 23

25 Geological interpretations are clearly affected by the uncertainties in geological data, themselves related to the data sets, data types or the density of the data points. However, the most prominent uncertainty source is that geological interpretations or geological modeling have a high degree of subjectivity. This is crucial when fixing the boundaries of the main geological objects. For instance, the interpretation of a given layer seen in a well has an uncertainty depending on the quality of the data collected in this well. Geological interpretation makes it possible to envision what is going on in between the wells. However, due to the high degree of subjectivity, the quantification of geological uncertainties is very difficult. 24

26 A typical example is depicted above. Assume that there are two wells drilled in a reservoir. Two facies are identified along these wells: thin beds of a tight facies, called Facies 1, and a facies with good reservoir properties, called Facies 2. Given this information, the problem consists in defining a geological concept. What is in between the wells? Do Facies 1 heterogeneities correspond to short, intermediate or extended barriers? How can we quantify the uncertainty associated to a given geological scenario? 25

27 The geological or reservoir models can be provided as inputs to flow simulators to estimate how production evolves with time. Some of the parameters describing these geological/reservoir models, such as fault sealing capacity and aquifer size are unimportant for the static point of view, but have strong implications for the dynamic behavior of the reservoir, that is production. These parameters are also subject to uncertainties: they comprise horizontal permeabilities (Kh), permeability anisotropy, ratio of vertical to horizontal permeabilities (Kv/Kh), relative permeabilities (kr) and capillary pressures (Pc), aquifer properties, fault properties, extension of barriers, well properties... The sources of uncertainties are related to the possible errors in measurements, but also to the non stationarity of these properties and to the lack of data. 26

28 In upfront reservoir engineering studies, it is essential to pay attention to the uncertainties in reservoir properties (geological and petrophysical data). Likewise, the uncertainties in fluid properties have to be accounted for as they strongly impact the accuracy of production forecasts. These uncertainties are mainly related to the lack of representative samples extracted from the reservoir and to the spatial variations in fluid properties within the reservoir. In addition, if focusing on the data available, a source of uncertainty is related to the way the properties of interest are measured in the laboratory. For instance, accurate measurements are quite difficult to obtain for heavy oil. Significant variations have been pointed out for heavy oil viscosity measured from different laboratories using different measuring techniques. Similarly, you have uncertainties in compositional analysis, volumetric measurements in the PVT laboratory. 27

29 The lack of data and the complexity of the formation processes make reservoir models and production forecasts uncertain. This uncertainty cannot be removed. Therefore, reservoir engineers resort to alternate techniques to account for it. They can use industryrecognized correlations between given properties. They can also investigate hydrocarbon production for probable scenarios. An alternative is to build reservoir models respecting the available data. This calls for geostatistics and history-matching. Geostatistics is a branch of statistics that studies spatial or spatiotemporal data. It is currently applied in petroleum geology, but also in hydrology, meteorology, oceanography, geochemistry, forestry, epidemiology... On the other hand, history-matching consists of adjusting a reservoir model until it reproduces (as well as possible) the production data collected in the past, that is the historical production. Broadly speaking, this problem is an inverse problem. Similar problems are also considered in many other disciplines such as hydrology, meteorology, oceanography... In the chapters hereafter, we focus on the data integration problem referring both to geostatistics and history-matching. 28

30 Until recently, data integration was considered as a sequential two-step process. 1- The first step, performed by geologists and geophysicists, was based upon the building of a geological model using geostatistical techniques. The result was a geological model populated by petrophysical properties respecting geological and geophysical data, but not production data (more generally speaking, dynamic data). 2- Then, this model was passed along to the reservoir engineer. 29

31 2- The reservoir engineer fed a flow simulator with this model and obtained numerical production responses. Most of the time, a significant discrepancy was observed between the responses and the data. 30

32 2- Therefore, the reservoir engineer made the required changes to compel this model to respect the production data. This history-matching process is an inverse problem. It is usually solved referring to optimization. However, at this stage, the main concern of the reservoir engineer was the production data solely: he somewhat disregarded the static data. At the end, the model, named reservoir model, could more or less reproduce the production data, but was likely to be inconsistent with respect to the geological and geophysical data. In this regard, the production profiles predicted from such a model are questionable. 31

33 Reservoir models typically fall into two categories: geological models or reservoir models. Geological models are created by geologists and geophysicists and aim to provide a static description of the reservoir, prior to production. Reservoir models (or reservoir simulation models) are created by reservoir engineers and provided to fluid flow simulators to simulate the flows of fluids within the reservoir, over its production lifetime. Sometimes, a single "shared earth model" is used for both purposes. More commonly, a geological model is constructed at a relatively high (fine) resolution. A coarser grid for the reservoir simulation model is constructed, with perhaps two orders of magnitude fewer cells. Effective petrophysical properties are then derived from the petrophysical properties populating the geological model by a process of "upscaling and these effective properties are assigned to the coarser reservoir model. 32

34 Reservoirs are very complex porous media due to the complexity of geological formation processes. Models are just approximate representations that make it possible to understand the spatial distribution of hydrocarbons and to identify the best production design to extract these hydrocarbons. The finely gridded geological model is populated with petrophysical properties (facies, porosity Phi, permeability k, fluid saturations sat and net-to-gross N/G). It is used to evaluate the volume of hydrocarbons initially in place (HCIIP) and its spatial distribution within the reservoir. The reservoir model is coarser for practical purposes. It is given as an input to the fluid flow simulator to predict fluid flows. Therefore, it encompasses less, but coarser grid blocks to make flow simulation feasible in a reasonable amount of time. The reservoir model has to be populated with additional petrophysical properties such as relative permeabilities kr and capillary pressures Pc. This model is used to help decision-makers to identify production designs optimizing the production of hydrocarbons. 33

35 The integration of data provided by various disciplines raises multiple issues. An integration process based upon two independent steps (geology and geophysics first, then reservoir) is not feasible in practice as it does not allow to preserve the consistency of the model with the geological, the geophysical and the production data together. An integrated modeling workflow has to be envisioned. An additional difficulty is that data are associated to different scales meaning that their integration has to be performed at different scales. This calls for complicated workflows with appropriate scale changes to make it possible to account for data at the right scale. Reservoir or geological models consist of millions of grid blocks to be populated with petrophysical properties. In other words, there are millions of unknown petrophysical property values and probably not enough data to characterize their spatial distributions with no uncertainty. 34

36 The traditional approach to data integration was linear or sequential with each discipline passing its model to the next one with no looking back for consistency checking. The new approach is dynamic. It is an iterative process including a sequence of modeling activities (Le Ravalec et al., 2012) with: - first, stochastic simulation tools to create the initial geological model, - then, upscaling tools to get the corresponding coarse reservoir model, - a flow simulator to simulate the flow responses to be compared to the available production data, and - a petro-elastic model to simulate the seismic responses to be compared to the available production and 4D-seismic data. This provides the framework for the model to be updated when any modeling parameter is modified. Iteration is a choice and can happen anywhere in the workflow. 35