WO2001073476A1 - Method for simulation of enhanced fracture detection in sedimentary basins - Google Patents

Abstract

A three-dimensional, geologic basin simulator for predicting natural resource location and characteristics is disclosed. The simulator integrates seismic inversion techniques with other data to predict fracture location and characteristics. The invention's 3-D finite element basin reaction, transport, mechanical simulator includes a rock rheology that integrates continuous deformation (poroelastic/viscoplastic) with fracture, fault, gouge, and pressure solution. Mechanical processes are used to coevolve deformation with multi-phase flow, petroleum generation, mineral reactions, and heat transfer to predict the location and producibility of fracture sweetspots. The simulator uses these physico-chemical predictions to integrate well log, surface, and core data with the otherwise incomplete seismic data. The simulator delineates the effects of regional tectonics, petroleum-derived overpressure, and salt tectonics and constructs maps of high-grading zones of fracture producibility.

Description

METHOD FOR SIMULATION OF ENHANCED FRACTURE DETECTION IN SEDIMENTARY BASINS

TECHNICAL FIELD

The present invention relates generally to three-dimensional modeling, and, more particularly, to modeling fractures in sedimentary basins in the context of resource exploration and production.

BACKGROUND OF THE INVENTION Interest in the remote detection of fractures in tight geologic reservoirs has grown naturally as new discoveries of petroleum and natural gas from conventional reservoirs have declined. The trend in remote detection is to invert seismic data. The problem is that such an inversion may not be possible in principle. For example, in an azimuthally anisotropic medium, the principal directions of azimuthal anisotropy are the directions along which the compressional and shear waves propagate. If anisotropy is due solely to fractures, anisotropy data can be used to study dominant fracture orientations. However, observed rose diagrams show that in most cases a fracture network consists of many intersecting fracture orientations.

A complete exploration and production (E&P) characterization of a fractured reservoir requires a large number of descriptive variables (fracture density, length, aperture, orientation, and connectivity). However, remote detection techniques are currently limited to the prediction of a small number of variables. Some techniques use amplitude variation with offsets to predict fracture orientations. Others delineate zones of large Poisson's ratio contrasts which correspond to high fracture densities. Neural networks have been used to predict fracture density. Porosity distribution may be predicted through the inversion of multicomponent three-dimensional (3-D) seismic data. These predictive techniques are currently at best limited to a few fracture network properties. Most importantly, these results only hold if the medium is simpler than a typical reservoir. For example, they may work if there is one fracture orientation and no inherent anisotropy due to sediment lamination or other inhomogeneity and anisotropy. Difficulties with remote fracture detection come from the many factors affecting mechanical wave speed and attenuation including: porosity and texture of unfractured rock; • density and phases of pore- and fracture-filling fluids; fracture length and aperture statistics and connectivity; fracture orientation relative to the propagation direction; fracture cement infilling volume, mineralogy, and texture; pressure and temperature; and gouge layers.

These variables cannot be extracted from the speed and attenuation of reflected or transmitted seismic waves, even when the various polarizations and shear vs. compression components are separately monitored. Thus, direct remote detection cannot provide enough information to unambiguously identify and characterize fracture sweetspots.

The petroleum industry requires information about the producibility of fracture networks: cement infilling; geometry, connectivity, density, and preferred orientation as well as parameters for dual porosity/dual permeability reservoir models; stress and reservoir sensitivity to pressure drawdown; petroleum content of the matrix; and fractures. While desirable for optimal exploration and petroleum field development, this level of detailed characterization is far beyond available remote detection methodologies.

SUMMARY OF THE INVENTION The above problems and shortcomings, and others, are addressed by the present invention, which can be understood by referring to the specification, drawings, and claims. The present invention is a 3-D basin simulator that integrates seismic inversion techniques with other data to predict fracture location and characteristics. The invention's 3-D finite element basin reaction, transport, mechanical simulator includes a rock rheology that integrates continuous deformation (poroelastic/viscoplastic) with fracture, fault, gouge, and pressure solutions. Mechanical processes are used to coevolve deformation with multi-phase flow, petroleum generation, mineral reactions, and heat transfer to predict the location and producibility of fracture sweet spots. The simulator uses these physico-chemical predictions to integrate well log, surface, and core data with the otherwise incomplete seismic data. The simulator delineates the effects of regional tectonics, petroleum- derived overpressure, and salt tectonics and constructs maps of high-grading zones of fracture producibility. BRIEF DESCRIPTION OF THE DRAWINGS

While the appended claims set forth the features of the present invention with particularity, the invention, together with its objects and advantages, may be best understood from the following detailed description taken in conjunction with the accompanying drawings of which:

Figure 1 is a depiction of the Simulation-Enhanced Fracture Detection approach;

Figure 2 is a table of the "laboratory" basins for use in reaction, transport, mechanical model testing; Figure 3 shows the coupled processes underlying the dynamics of a sedimentary basin;

Figure 4a depicts the fracture healing cycle; Figure 4b show the Ellenburger overpressure oscillation; Figure 5 is a simulation from the Piceance Basin; Figures 6a, 6b, and 6c show predictions from the Piceance Basin;

Figure 7 shows predicted rose diagrams for the Piceance Basin; Figures 8a and 8b are simulations of the Piceance Basin; Figures 9a and 9b are normal fault simulations; Figure 10 shows an oil saturation/salt dome; Figure 11 is a simulation of subsalt oil;

Figure 12 is a simulation of a salt diapir;

Figure 13 is a flow chart of a basin reaction, transport, mechanical model; Figures 14a and 14b show a prediction of Andector Field fractures; Figure 15 is a table of input data available for the Illinois Basin; Figure 16 shows a simulation of the Illinois Basin;

Figure 17 shows the 3-D stratigraphy of the Illinois Basin; Figure 18 is a map of the Texas Gulf coastal plain;

Figure 19 is a map of producing and explored wells along the Austin Chalk trend; and Figure 20 is a generalized cross-section through the East Texas Basin.

DETAILED DESCRIPTION OF THE INVENTION Turning to the drawings, the invention is illustrated as being implemented in a suitable environment. The following description is based on embodiments of the invention and should not be taken as limiting the invention with regard to alternative embodiments that are not explicitly described herein.

Technical Overview of Simulation-Enhanced Fracture Detection The present invention enhances seismic methods by using a 3-D reaction, transport, mechanical (RTM) model called Basin RTM. Remote observations provide a constraint on the modeling and, when the RTM modeling predictions are consistent with observed values, the richness of the RTM predictions provides detailed data needed to identify and characterize fracture sweetspots (reservoirs). This simulation- enhanced fracture detection (SEFD) scheme is depicted in Figure 1. SEFD makes the integration of remote measurement and other observations with modeling both efficient and "seamless."

The SEFD algorithm has options for using raw or interpreted seismic data. The output of a 3-D basin simulator, Basin RTM, is lithologic information and other data used as input to a synthetic seismic program. The latter' s predicted seismic signal, when compared with the raw data, is used as the error measure E as shown in Figure 1. Similarly, well logs and other raw or interpreted data shown in Figure 1 can be used. The error is minimized by varying the least well-constrained basin parameters.

The SEFD method integrates seismic data with other E&P data (e.g., well logs, geochemical analysis, core characterization, structural studies, and thermal data). Integration of the data is attained using the laws of physics and chemistry underlying the basin model used in the SEFD procedure: conservation of momentum (rock deformation, fluid flow); • conservation of mass (fluid species and phases, and mineral reactions and transport); and • conservation of energy (heat transfer and temperature).

These laws facilitate extrapolation away from the surface and wellbore and are made consistent with seismic data to arrive at the SEFD approach shown in Fig. 1.

The SEFD model is calibrated by comparing its predictions with observed data from chosen sites. Calibration sites meet these criteria: sufficient potential for future producible petroleum, richness of the data set, and diversity of tectonic setting and lithologies (mineralogy, grain size, matrix porosity). Figure 2 lists several sites for which extensive data sets have been gathered. Basin RTM attains seismic invertibility by its use of many key fracture prediction features not found in previous basin models:

• nonlinear poroelasticity/viscosity rheology with integrated pressure solution, fracture strain rates, and yield behavior for faulting; • a full 3-D fracture network statistical dynamics model; rheologic and multi-phase parameters that coevolve with diagenesis, compaction, and fracturing;

• new multi-phase flow and kerogen reactions producing petroleum and affecting overpressure; • tensorial permeability from preferred fracture orientation and consequent directed flows;

• inorganic fluid and mineral reactions and organic reactions; and heat transfer.

While previous models have some of these processes, none have all, and none are implemented using full 3-D finite element methods. Basin RTM preserves all couplings between the processes shown in Figure 3. The coupling of these processes in nature implies that to model any one of them requires simulating all of them simultaneously. As fracturing couples to many RTM processes, previous models with only a few such factors cannot yield reliable fracture predictions. In contrast, the predictive power of Basin RTM, illustrated in Figures 4 through 9 and discussed further below, surmounts these limitations.

Commonly observed "paradoxes" include fractures without flexure and flexure without fractures. These paradoxes illustrate the inadequacy of previous fracture detection techniques based on statistical correlations. For example, previous models base porosity history on a formula relating porosity to mineralogy and depth of burial. However, porosity evolves due to the detailed stress, fluid composition and pressure, and thermal histories of a given volume element of rock. These histories are different for every basin. Thus, in the real world, there is no simple correlation of porosity with depth and lithologic type. As shown in Figure 3, aspects of geological systems involve a multiplicity of factors controlling their evolution. Some processes are memory- preserving and some are memory-destroying. There are no simple correlations among today's state variables. The detailed history of processes that operated millions of years ago determines today's fracture systems. Basin RTM avoids these problems by solving the fully coupled rock deformation, fluid and mineral reactions, fluid transport and temperature problems (Figures 3 and 13). Basin RTM derives its predictive power from its basis in the physical and chemical laws that govern the behavior of geological materials. As salt withdrawal is an important factor in fracturing in some basins, Basin

RTM models salt tectonics. Basin RTM addresses the following E&P challenges: predict the location and geometry of zones of fracturing created by salt motion;

• predict the morphology of sedimentary bodies created by salt deformation;

• locate pools of petroleum or migration pathways created by salt tectonics; and • assist in the interpretation of seismic data in salt tectonic regimes.

The interplay of salt deformation with the rheology of the surrounding strata is key to understanding the correlation between salt deformation and reservoir location. Figures 10 through 12 show simulation results produced by Basin RTM.

Details of an Exemplary Embodiment A complex network of geochemical reactions, fluid and energy transport, and rock mechanical processes underlies the genesis, dynamics, and characteristics of petroleum reservoirs in Basin RTM (Figure 3). Because prediction of reservoir location and producibility lies beyond the capabilities of simple approaches as noted above, Basin RTM integrates relevant geological factors and RTM processes (Figure 13) in order to predict fracture location and characteristics. As reservoirs are fundamentally 3-D in nature, Basin RTM is fully 3-D.

The RTM processes and geological factors used by Basin RTM are described in Figures 3 and 13. External influences such as sediment input, sea level, temperature, and tectonic effects influence the internal RTM processes. Within the basin, these processes modify the sediment chemically and mechanically to arrive at petroleum reserves, basin compartments, and other internal features.

Basin RTM predicts reservoir producibility by estimating fracture network characteristics and effects on permeability due to diagenetic reactions or gouge. These considerations are made in a self-consistent way through a set of multi-phase, organic and inorganic, reaction-transport and mechanics modules. Calculations of these effects preserve cross-couplings between processes (Figures 3 and 13). For example, temperature is affected by transport, which is affected by the changes of porosity that changes due to temperature-dependent reaction rates. Basin RTM accounts for the coupling relations among the full set of RTM processes shown in Figure 3.

Key elements of the dynamic petroleum system include compaction, fracturing, and ductile deformation. These processes are strongly affected by basin stress history. Thus, good estimates of the evolution of stress distributions are useful in predicting these reservoir characteristics. As fracturing occurs when fluid pressure exceeds least compressive stress by rock strength, estimates of the time of fracture creation, growth, healing or closure, and orientation rely on estimates of the stress tensor distribution and its history. Simple estimates of least compressive stress are not sufficient for accurate predictions of fracturing and other properties. For example, least compressive stress can vary greatly between adjacent lithologies — a notable example being sandstones versus shale (see Figures 6 and 7). In Basin RTM, stress evolution is tightly coupled to other effects. Fracture permeability can affect fluid pressure through the escape of fluids from overpressured zones; in turn, fluid pressure strongly affects stress in porous media. For these reasons, the estimation of the distribution and history of stress must be carried out within a basin model that accounts for the coupling among deformation and other processes as in Figure 3.

A rock rheological model based on incremental stress theory is incorporated into Basin RTM. This formalism has been extended to include fracture and pressure solution strain rates with elastic and nonlinear viscous/plastic mechanical rock response. This rheology, combined with force balance conditions, yields the evolution of basin deformation. The Basin RTM stress solver employs a moving, finite element discretization and efficient, parallelized solvers. The incremental stress rheology used is ^ JX + £? + J£^S -I- X . Here is the net rate of strain while the terms on the right hand side give the specific dependence of the contributions from poroelasticity {el), continuous inelastic mechanical (in), pressure solution (ps), and fracturing (fr). The boundary conditions implemented in the Basin RTM stress module allow for a prescribed tectonic history at the bottom and sides of the basin.

The interplay of overpressuring, methanogenesis, mechanical compaction, and fracturing is illustrated in Figure 4a. In this Piceance Basin simulation, fracturing creates producibility in the sandstones lying between the shales. In Figure 4b, a similar source rock in the Ellenburger of the Permian Basin (West Texas) is seen to undergo cyclic oil expulsion associated with fracturing.

In Figures 9a and 9b, the results of Basin RTM show fault-generated fractures and their relation to the creation of fracture-mediated compartments and flow. This system shows the interplay of stress, fracturing, and hydrology with overall tectonism — features which give Basin RTM its unique power.

A key to reservoirs is the statistics of the fracture network. Basin RTM incorporates a unique model of the probability for fracture length, aperture, and orientation. The model predicts the evolution in time of this probability in response to the changing stress, fluid pressure, and rock properties as the basin changes. The fracture probability formulation then is used to compute the anisotropic permeability tensor. The latter affects the direction of petroleum migration, information key to finding new resources. It also is central to planning infill drilling spacing, likely directions for field extension, the design of horizontal wells, and the optimum rate of production.

Figure 14 shows a simulation using Basin RTM for Andector Field (Permian Basin, West Texas). Shown are the orientations of the predicted vertical fractures with their distribution across the basin.

The fracture network is dynamic and strongly lithologically controlled. Figure 7 shows fracture length-orientation diagrams for macrovolume elements in two lithologies at four times over the history of the Piceance Basin study area. The fractures in a shale are more directional and shorter-lived; those in the sandstone appear in all orientations with almost equal length and persist over longer periods of geological time. The 3-D character of the fractures in this system is illustrated in Figures 5 and 8.

Modules in Basin RTM compute the effects of a given class of processes (Figures 3 and 13). The sedimentation/erosion history recreation module takes data at user-selected well sites for the age and present-day depth, thickness, and lithology and creates the history of sedimentation or erosion rate and texture (grain size, shape, and mineralogy) over the basin history. The multi -phase and kerogen decomposition modules add the important component of petroleum generation, expulsion, and migration (Figures 6, 11, and 12). Other modules calculate grain growth/dissolution at free faces and grain-grain contacts (e.g., pressure solution). The evolution of temperature is determined from the energy balance. All physico-chemical modules are based on full 3-D, finite element implementation. As with the stress/deformation module, each Basin RTM process and geological data analysis module is fully coupled to the other modules (Figures 3 and 13). Geological input data is divided into four categories (Figure 13). The tectonic data gives the change in the lateral extent and the shape of the basement-sediment interface during a computational advancement time Dt. Input includes the direction and magnitude of extension/compression and how these parameters change through time. These data provide the conditions at the basin boundaries needed to calculate the change in the spatial distribution of stress and rock deformation within the basin. This calculation is carried out in the stress module of Basin RTM.

The next category of geological input data directly affects fluid transport, pressure, and composition. This includes sea level, basin recharge conditions, and the composition of fluids injected from the ocean, meteoric, and basement sources. Input includes the chemical composition of depositional fluids (e.g., sea, river, and lake water). This history of boundary input data is used by the hydrologic and chemical modules to calculate the evolution of the spatial distribution of fluid pressure, composition, and phases within the basin. These calculations are based on single- or multi-phase flow in a porous medium and on fluid phase molecular species conservation of mass. The physico-chemical equations draw on internal data banks for permeability-rock texture relations, relative permeability formulae, chemical reaction rate laws, and reaction and phase equilibrium thermodynamics.

The spatial distribution of heat flux imposed at the bottom of the basin is another input to Basin RTM. This includes either basin heat flow data or thermal gradient data that specify the historical temperature at certain depths. This and climate/ocean bottom temperature data are used to evolve the spatial distribution of temperature within the basin using the equations of energy conservation and formulas and data on mineral thermal properties.

Lithologic input includes a list and the relative percentages of minerals, median grain size, and content of organic matter for each formation. Sedimentation rates are computed from the geologic ages of the formation tops and decomposition relations. The above-described geological input data and physico-chemical calculations are integrated in Basin RTM over many time steps Dt to arrive at a prediction of the history and present-day internal state of the basin or field. Basin RTM's output is rich in key parameters needed for choosing an E&P strategy: the statistics of fracture length, orientation, aperture, and connectivity, in situ stress, temperature, the pressure and composition of aqueous and petroleum phases, and the grain sizes, porosity, mineralogy, and other matrix textural variables.

The continuous aspects of the Basin RTM rheology for chalk and shale lithologies are calibrated using published rock mechanical data and well-studied cases wherein the rate of overall flexure or compression/extension have been documented along with rock texture and mineralogy. Basin RTM incorporates calibrated formulas for the irreversible, continuous and poroelastic strain rate parameters and failure criteria for chalk and shale needed for incremental stress rheology and the prediction of the stresses needed for fracture and fault prediction. The texture model incorporates a relationship between rock competency and grain-grain contact area and integrates the rock competency model with the Markov gouge model and the fracture network statistics model to arrive at a complete predictive model of faulting.

Basin RTM's 3-D grid adaptation scheme (1) is adaptive so that contacts between lithologic units or zones of extreme textural change (i.e., narrow fault zones) are captured; and (2) preserves all lithologic contacts.

In the SEFD approach, Basin RTM is optimized whereby parameters that are key to the predictions, yet are less well-known, are computed by (1) generating a least- squares error (that represents the difference between the actual data and that predicted by Basin RTM and seismic recreation programs), and (2) minimizing the error using a conjugate gradient or other approach. Software implementing the SEFD techniques is optimized by: parallelizing sparse matrix solvers; • multi-timing whereby variables that change more slowly "wait" several computational time-steps while faster ones are advanced; and optimizing convergence criteria for various modules to obtain the best compromise for overall program speed and accuracy. Sample Cases: The New Albany Shale, Antrim Shales, the Austin Chalk, and Piceance and West Texas Basins

Basin RTM's ability to predict and characterize fractures may be shown by comparing observed fracture locations and characteristics with those predicted by the Basin RTM/SEFD approach. The sensitivity of the results to noise in the seismic data or other data uncertainties show the robustness of the approach. The effects of the uncertainties in the basin history parameters on the prediction of fracture characteristics, fluid pressure, porosity, and temperature are also examined. The overall (multi-process) dynamics of Basin RTM are compared with geological data on sample lithologies. Calibration is performed in an iterative fashion (simulate, recalibrate, repeat) for one or more fields such as those from the Austin Chalk, Piceance and West Texas Basins, and the Antrim Shale. Testing success is measured by assessing the percentage error between the SEFD-predicted and observed locations and properties of the reservoirs. These properties include fracture intensity, orientation and connectivity, reservoir permeability and other flow characteristics from production data, petroleum composition and reserve estimates, stresses and matrix properties (mineralogy, grain size, composition, grain breakage), and reservoir temperature.

As a first example of the use of the SEFD technique, consider the Illinois and Michigan Basins, especially the New Albany and Antrim Shales. Abundant well control and other data are available for these basins. Figure 15 summarizes the Illinois Basin data set available. A similar richness of data exists for the systems of Figure 2. Input files for Basin RTM were compiled from these sources to determine the suitability of the available data (Figure 16). Two wells are the focus of preliminary 1- D simulations, the Unocal No. 1 Cisne in Wayne County, Illinois, and the Indiana Farm Bureau No. 1 Brown in Lawrence County, Indiana. Simulations produced by Basin RTM revealed the evolution of porosity. The results show fracture enhancement of permeability during the last 200 million years of basin evolution (Figure 16) and indicate that much new information can be learned about fracture location and characteristics through SEFD. Figure 17 shows a Basin RTM-constructed 3-D section.

The second example, the Austin Chalk (AC), is a prolific, apparently self- sourced, formation in the onshore U.S. Gulf Coast (Figure 18). As gas and oil producing zones (Figure 19) are typically of low matrix permeability, fracture sweetspots are key to producibility. The difficulty in locating the latter is a serious limitation to the development of this resource.

Large fractured reservoir systems are present in the Giddings and Pearsal Field areas and throughout the East Texas Basin (Figure 20). The fractures have been attributed in part to petroleum generation. However, these fields are interspersed with and are surrounded by other fracture systems whose regularity is not always obvious. The possibilities of ancient controls related to salt motion should also be considered (Figure 20) along with deeper-lying faults, thermal anomalies, and the overall extensional tectonics. Model-derived mapping of the aforementioned factors facilitates exploration and exploitation in this system.

The AC is one of the higher-lying formations in this play. The Jurassic Smackover limestone is very close to the salt. In fact, lower in the Texas Gulf Coast, salt diapirs directly affect the Smackover. Thus, it might be possible to locate other fracture plays that salt withdrawal may have created deep in the section. The SEFD mapping are useful in lease acquisition and planning. Mapping of these fracture zones and fixing their time of formation is an important part of the SEFD prospectivity analysis. These likely subtle fracture systems are discernible remotely with the insight of the forward, dynamic fracture modeling and SEFD approach.

In view of the many possible embodiments to which the principles of this invention may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of invention. Therefore, the invention as described herein contemplates all such embodiments as may come within the scope of the following claims and equivalents thereof.

Claims

CLAIMSWe claim:

1. A method for producing a three-dimensional map of fracture locations and characteristics in a geological basin, the method comprising: collecting data pertaining to characteristics of the geologic basin; simulating rock rheology by integrating continuous deformation with fracture, fault, gouge, and pressure solutions; • simulating mechanical processes to coevolve deformation with multiphase flow, petroleum generation, mineral reactions, and heat transfer to predict the location and producibility of fracture sweetspots; adjusting the predictions to reduce their deviation from the collected data; and integrating the resulting predictions with the collected data to construct maps of high-grading zones of fracture producibility.

2. The method of claim 1 wherein collecting data includes collecting data in the set: well log data, surface data, core data, seismic data.

3. A computer-readable medium having instructions for performing the method of claim 1.