SCRF the art of building on fundamentals

Transcription

1 SCRF the art of building on fundamentals The SCRF crowd at the Getty Museum, Los Angeles, February 2004 It is a recurrent pleasure to report to you every year about research at SCRF. We have completed yet another year with exciting new ideas and practical results. This year is marked by an increased breath in research topics. Browsing to the list of papers you will notice, next to our usual menu of mp-geostatistics and history matching papers, new topics such as 4D seismic and fracture modeling. The latter are two critical topics in reservoir modeling that have not been yet one of our focus research topics. Why do we feel ready for to tackle these topics? Consider the following rationale. Since its inception, SCRF has aimed at breaking new avenues, developing new methodologies for reservoir characterization with applications to actual reservoirs. The two major lines of research over the last two years have been (1) multiple-point/patternbased geostatistics and (2) history matching under geological control. Consider now the problem of predicting water saturation changes from 4D seismic. Any research on 4D seismic will necessarily have to deal with the fact that the relation between amplitude difference and saturation changes is necessarily multiple-point, in other words, it is a pattern relationship. In last year SCRF report Jianbing Wu demonstrated that any correlation between time lapse seismic and saturation is one of spatial patterns, not a traditional correlation between single values as characterized by a correlation coefficient: the connection with mp-geostatistics was made. In this year SCRF report, Jianbing continues by demonstrating that 4D seismic data are dynamic data, just like production data, in that they inform not specific locations (except in trivial cases where geostatistics is not needed) but the whole volume between injectors and producers. The connection is

2 made with our history matching line of research. Various ideas are currently being explored. 4D seismic Jianbing Wu provides a preliminary study on how 4D seismic should be used in the general and difficult case where fingering or channeling is not visible. 4D seismic should then be used to improve the static geological model. He shows that any difference between the 4D seismic time lapse and flow simulation-predicted water saturations at any location in the reservoir need not be due to modeling errors at that location, but is the result of accumulative modeling errors, accumulated over all grid blocks flooded from the injector. 4D seismic data is therefore intrinsically dynamic in nature (not static) and may provide indications on the quality of the initial reservoir model. However, it may be difficult to discern the actual sources of the modeling errors. Modeling error could be due to a poor geostatistical model or due to poor 3D seismic. Scarlet Castro, as an MSc graduate from our own Geophysics department has joined SCRF for a PhD, she is in an excellent position to undertake the challenge brought forward by Jianbing. Her approach takes the route of history matching by regarding time lapse seismic as an additional piece of dynamic data. However, her approach is quite different from current history matching methods that rely on a fixed or assumed known static model. Scarlet extends on the history matching workflow proposed by Inanc Tureyen (SCRF 2003). A fine scale realization constrained to well-log and 3D seismic is generated, then upscaled to a coarse model realization. Flow simulations on the coarse scale provide saturation distributions necessary to simulate the time lapse response. Next, and this is crucial, any model perturbations are made on the fine scale in order to better match the time-lapse field response. All fine scale model perturbations are made geologically consistent by means of the probability perturbation method (SPE 74716, SCRF 2002). This guarantees that the resulting model will be constrained to all static (well-log and seismic) and dynamic data (4D and production) in a consistent fashion. Fracture modeling The significance of fractured carbonate reservoirs in the global pool of Oil and Gas reservoirs will only continue to increase. One of the most challenging problems in fracture modeling is the simulation of flow. It does not make a lot of sense to develop sophisticated static fracture models without dedicating the same effort to understand and model flow in non random fractured media. Current flow simulation models include upscaling models, dual porosity/dual permeability models (DP and DPDP) and discrete fracture models (DFN). DFN finite element codes are not yet widely used and few commercially tested, robust and accepted are in industry. Most DFN models cannot handle more than a few hundred fractures when considering two-phase flow. DP or DPDP are only adequate under certain types of fracturing, i.e. when fracture flow is not the dominant flow.

3 In the long run, SCRF aims at providing an integrated solution to this problem, i.e. consider simultaneous static modeling and fracture flow simulation. Our intention is certainly not to generate yet another static modeling tool that is detached from the flow simulation challenge. Joe Voelker turns the question around: what in fractures matters most for flow? Then, how should they be modeled both statically and dynamically? This top-down engineering approach, i.e. purpose driving the approach, clearly depends on the type of fracture reservoirs under study. Joe is motivated by his study on Ghawar, where he showed (SCRF 2003) that only the inclusion of prolific fracture networks can explain super-k behavior observed through flowmeter tests. Joe proposes to model directly any flow originating from fracture networks instead of individual fractures. The advantage is clear: there are much fewer networks than fractures and the flow impact of these networks is addressed frontally. Joe s well-model (simply a set of extra connections specified into the flow simulation run) allows him to integrate these fracture networks in existing finite difference simulators. He then history matches pressure and flow data by perturbing the pseudo-well properties, i.e. he perturbs their amount, regional variation, and geometric properties. Yongshe Liu takes a different route, that of directly correcting effective grid block properties to account for subseismic scale fractures. In his approach, fractures are never explicitly modeled, only their influence on grid block porosity and permeability is considered. To evaluate the impact of such subseismic fractures, Yongshe selects a calibration panel (much smaller than the reservoir), then simulates fracture aperture (FA) and spacing (FS) within the panel on a very fine grid. Next, FA and FS are upscaled to block scale FA and FS properties within that panel. The statistics of these block properties are then used to simulate FS and FA within the entire reservoir unit. Porosity and permeability are retrieved from established rock physics relations. He then shows that the influence of subseismic fractures on primary and secondary recovery can be substantial. Multiple-point geostatistics This year report al includes new developments on what is now a trademark of SCRF, looking at geological heterogeneities through patterns as opposed to a series of single points or voxels. The concept of multiple-point geostatistics was introduced by Andre Journel and Mohan Srivastava as far back as 1992, but really broke out with the thesis of Sebastien Strebelle (2000) and his widely acknowledged simulation code snesim, or, single normal equation simulation. The snesim code has now been tested by several member companies and has withstood the test in many diverse geological environments. At SCRF we have conducted vigorous research into various approaches to improve snesim in terms of CPU/RAM demand as well as quality of training image pattern reproduction. Currently, we are at an important junction, a next level, in the development of mp-geostatistics. This says that pattern-based mp-geostatistics is still in its infancy.

4 The main limitation of snesim lies in its requirement of stationarity of the training image. In snesim, training images are used for the calculation of conditional probabilities of a central location state given the pattern information provided by some neighboring data, called data event :. That probability is calculated by scanning the training image for replicates of the same data event. This requires a form of stationarity (translation invariance of mp-statistics) of the training image patterns that limits which training images can be used. Several solutions have been provided to address this problem: define regions with different facies proportions or different training images. A locally varying anisotropy can be introduced locally rotating the training image to match local directions of continuity, as, e.g. interpreted from seismic data (Caers and Zhang, SCRF 2001). In this SCRF report, three new mp-algorithms are presented. Simpat, developed by Burc Arpat was originally conceived as an improvement of snesim, for obtaining better pattern reproduction. Indeed, the paper of Burc Arpat in SCRF 2003 shows that a simpat-like algorithm can reproduce thin, highly meandering channel systems. We soon came to realize that simpat is more than just an improvement of snesim, it may be a completely new way of looking at mp-geostatistics. The simpat algorithm relies on the new concept,of similarity and does not draw on probability theory. It simply copies patterns from the training image directly into the reservoir model. The sequence of this copying is made random hence realizations are generated. Simply copying patterns may sound disconcerting at first hear, because there is nothing left to randomness that is to the probabilistic model, everything would come from the training image, the patterns of which are neither filtered nor changed. The simpat approach relies indeed heavily on the quality of the training image in providing a relevant and very rich collection of training patterns deemed existing in the subsurface. The main advantage of simpat is that it does allow for non-stationary training images: any non-stationary, non-repeated pattern of the training image can be directly exported to the reservoir model, a feature that may appeal to practitioners. Because of its conceptual difference from snesim, we are only starting to understand the significance of the simpat approach. I hope to convey this new understanding in my introductory paper to this 2004 meeting. Tuanfeng Zhang is also exploring an avenue different from the mp-geostatistics coded in snesim, although he does stay within the traditional probabilistic mold. Arguing that one only needs to export the essence of a training image, Tuanfeng summarizes the training image patterns by a few general linear filters. Applying this set of filters to the training image allows classifying all training image patterns into the low-dimensional filter scores space, thus reducing RAM demand, another drawback of snesim. Such filter scores measure the level of mean, gradient and curvature of training image patterns and provide a low-dimensional representation of multi-point statistics. Sequential simulation then proceeds in a style similar to simpat: using a distance each data event is identified to a closest score class, a training pattern is drawn from that score class and pasted directly into the grid. Amisha Maharaja presents a simple yet extremely useful extension of the traditional snesim algorithm. Because joint simulation of a large number of facies often leads to

5 poor pattern reproduction (more facies means more RAM in snesim), she proposes a method for hierarchically simulating facies and applies it to a fluvial channel reservoir. The grouping of facies and the sequence of simulations should be guided by geological rules of deposition and erosion. History matching This 2004 report presents the results of our continuing research focus in history matching under geological control. Todd Hoffman presents a North Sea case study where the regional variation and local positions of calcite bodies are determined from production data. He relies on the regional probability perturbation method presented in SCRF His case study shows how conceptual methodologies such as the probability perturbation, developed at SCRF, have evolved fast into actual reservoir history matching practice. Todd s work is done in close collaboration with Statoil, demonstrating that transfer of methods does not only come through a software code, but also through personal interaction with SCRF students. Streamlines have proven useful in addressing complex history matching problems. Streamlines are ideal for detecting/targeting highly heterogeneous patterns in the permeability field. Moreover, streamlines have the ability to visualize the flow field and indicating, by means of time-of-flight, what region of the permeability influences most which time in the production data. Herve Gross relies on these streamline properties to apply a streamline-based history matching (SCRF ) to a large and mature Middle Eastern carbonate field. Herve s work carries the same significant message of data integration as Todd s: he showed that with some practical geostatistical thinking, and with information gathered from streamlines, the difficult task of history matching a complicated and mature field can be achieved in less than five weeks. Again, the interaction with geologist and engineers in Dubai proved crucial. We would also like to acknowledge the involvement of Marco Thiele (Streamsim). While petrophysical properties are important parameters to adjust in a history matching procedure, structural elements, such as horizon and faults are the most important parameters, they define the major plumbing of the reservoir. In her PhD, Satomi Suzuki will attempt to design methods for history matching by perturbing these first order structures. In her SCRF paper, Satomi presents a mechanism for jointly perturbing faults and horizons to match. She starts by assuming the fault position known, perturb fault transmissibilities by perturbing the fault displacement. To account for static information (well and 3D seismic), fine-scaled reservoir models need to be generated; However, in most practical cases, on cannot run a single flow simulation on such fine scale realization, let alone multiple ones, as required by traditional iterative history matching procedure. Inanc Tureyen proposed a solution to this problem last year by making an optimized upscaling a part of the history matching procedure. This year he includes 3D DEGA into the upscaling methodology and extends his techniques to 3D. 3D DEGA is an elastic gridding method, developed at SCRF by

6 Michel Garcia in Michel improved the code over the last ten years, added additional features and made it compatible with commercial flow simulators. He visited us later December with the improved code which is available for download on our website. An accompanying paper by Inanc provides the essential technical background as well as some example parameter files. Uncertainty One of the most difficult uncertainty to assess quantitatively, yet possibly the most critical one at least in terms of monetary impact is that related to global reserves (volume and net-to-gross N/G) in an early development phase when a few or a single appraisal well is available together with a seismic survey of yet uncertain quality. Guillaume Caumon, a freshly minted PhD from the gocad group of Prof. Mallet, has undertaken that challenge as a postdoc here at SCRF on a fellowship provided by ChevronTexaco. Guillaume argues that in such early phase, the most critical source of uncertainty is the geological interpretation of the few data available including seismic. He proposes using alternative training images from which a spatial bootstrap can be developed. As opposed to the traditional bootstrap, spatial bootstrap allows (simulated) re-sampling of the data under any specific geological scenario of data dependence. Probabilistic data integration One could argue, as Andre Journel did in SCRF 2002, that the single most important roadblock of any prediction endeavor is to know how to integrate data of different sources, whether deterministically or probabilistically. The challenge is to not assume data as independent, since they never are in practice. Consider the evaluation of an event A from various data types D i, i = 1, n. Proper calibration provides for each datum Di, the probability P(A D i ); the critical problem is how these n pre-posteriors distributions should be combined into a single posterior distribution P(A D 1,D 2,.D n ). Sunderrajan Krishnan, building on the τ model introduced by Andre in SCRF 2002, suggested evaluating the τ data redundancy parameters from, first ranking the n data D i according to their information content based on P(A D i ), then calculating the parameters τ i from the correlation of each datum D i with the most informative one D 1. Sunder provides clues about how to get such correlation when dealing with complex data events GEMS: Geostatistical Earth Modeling Software During this year s SCRF meeting, you will be able to test version 1.0 of our new software GEMS, developed by Nicolas Remy. GEMS provides an integrated environment for parameter interface and 3D visualization. The code is not a substitute for commercial software. Rather, it provides us a unified platform to easily transfer new algorithms to the

7 affiliates. The GEMS label is a quality mark that will only be attached to those algorithms that we belief are proven and robust. Nicolas will hand out a preliminary User s Manual, actually a chapter of his PhD thesis, which he will defend in the Fall of Other news Our students keep stacking up awards. A second year in a row (after Rami Younis in 2002), a major award was clinched at the SPE ATCE (2003): Burc Arpat won the SPE international student paper in the PhD division in Denver. Inanc Tureyen took second spot in the Western Regional meeting Bakersfield last month. Joe Voelker was awarded the H. Ramey award for most outstanding PhD student in our own Petroleum Engineering Department. Joe again was awarded a very competitive fellowship by the RPSEA (Research Partnership to Secure Energy for America): only five candidates are awarded fellowships every semester. We also welcome two new members to SCRF: Schlumberger and E&P Tech. Last but not least I would like to draw your attention to the various presentations by affiliate members. Unfortunately, this year we had to limit the number of member presentations. Our apologies to those who had offered to present, but our students take priority. I would also like to mention that we benefited this year from a three month visit by Albert Tarantola from the Paris Institut de Physique du Globe. Albert is the father of Inverse Modeling. His class on Inverse Theory was extremely well received as were the often vigorous, ta(ra)ntalizing, but always refreshing debates we had with Albert during this period. We hope you will enjoy this meeting and, as always, we are looking forward to your remarks, criticisms and comments, Jef Caers, Stanford, April 20, 2004