Modelado 3D de la estructura interna del volcán Poás, Costa Rica: uso del software Leapfrog Geo

This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and the Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling. In period 2015 - 2018 a large bulk of geological and field development data was collected in the Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well tests, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, results of well interventions). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, e.g. seismic, well logs and core data, reservoir pressures and oil fingerprinting. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, depositional model and reservoir correlation were updated based on sequential stratigraphy principles. As a result, a new static model of the main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1’ and XXI-2. To check a new concept of geological architecture, material balance model was built and matched to the field data Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new wells correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests the essential improvement of reservoir modelling process by inclusion of the various well and reservoir surveillance data. This paper consists of the following sections: Introduction ∘ Field geology ∘ Field development history Scope of work complete and main results ∘ Proposed well logs correlation update for XXI-1’ and XXI-2 layers ▪ Integration of well logs, pressure and fluid analysis data ∘ Connectivity between layers XXI-S, XXI-1’ and XXI-2 ▪ Integration of pressure and oil fingerprinting data ∘ Connectivity within layers XXI-S, XXI-1’ and XXI-2 ▪ Results of pressure interference tests ∘ Testing of new well correlation concept in material balance model ∘ Proposed reservoir correlation based on seismic data ∘ New geological concept ▪ New depositional model ▪ Integration of core data ▪ Changes in reservoir architecture Conclusion ∘ Main results and impact on field development

4D seismic is important information to optimize well planning, interventions and drainage strategy. It provides unique information regarding the properties of the reservoir between and beyond the wells; it can be used to locate bypassed oil, to identify not drained compartments or to mitigate risks such as an unexpectedly early breakthrough of injected fluids. The integration of geophysics and engineering information increase the predictive capability of reservoir simulations, and hence has positive impacts on field management. The estimation of the best time to acquire seismic data has a major impact on reservoir management since, depending on the acquisition period, seismic may not be able to identify important variations in the reservoir dynamic properties, important changes may no longer be detected, well history matching can be able to identify properties, and it can be too late for effective action in field management. 4D seismic reservoir monitoring has no value unless it impacts operations and its utility is related to the period of seismic data acquisition. The challenge is to use effectively the available information; the integration of geophysics and engineering information must be accomplished by performing the history matching process that incorporates both production and 4D seismic data. Changes in saturation and pressure, derived from 4D seismic, can be used to improve the quality of the reservoir model allowing a better understanding of the reservoir model and fluid flow. Given the importance in defining the time for seismic data acquisition, this paper evaluates the quality improvement of a synthetic reservoir model by incorporating seismic data over different production periods and identifies characteristics that impact on the best time estimation. It also, estimates the best time to the case studied considering a practical condition. The study evaluated two cases: a posteriori (Case 1) and a priori (Case 2) analysis. In Case 1 the true model is known; a history match process was performed to 2, 4 and 6 years of production using two methods considering: (1) only production data; (2) production and seismic data. By analyzing the reservoir model quality improvement it was possible to identify the main characteristics that determine the moment to acquire seismic data. As, in practice, the earth true model is unknown, in Case 2, a process flow considering a risk analysis associated with reservoir simulation was applied to estimate the best time to acquire 4D seismic information. Analyzing the reservoir model quality improvement it was possible to identify the main characteristics that determine the best moment to acquire seismic data, to a non-mature oil reservoir with water injection production mechanism. These characteristics include: saturation and pressure error maps, production data error and breakthrough distribution within production wells.

Reservoir characterization and modeling have become increasingly important for optimizing field development. Optimal valuation and exploitation of a field requires a realistic description of the reservoir, which, in turn, requires integrated reservoir characterization and modeling. An integrated approach for reservoir modeling bridges the traditional disciplinary divides and tears down interdisciplinary barriers, leading to better handling of uncertainties and improvement of the reservoir model for field development. This article presents the integration of seismic data using neural networks and the incorporation of a depositional model and seismic data in constructing reservoir models of petrophysical properties. Some challenging issues, including low correlation due to Simpson's paradox and under- or overfitting of neural networks, are mitigated in geostatistical analysis and modeling of reservoir properties by integrating geologic information. This article emphasizes the integration of well logs, seismic prediction, and geologic data in the 3D reservoir-modeling workflow.

Over the last project six months, our project activities have concentrated on three areas: (1) performing a stochastic inversion of pattern 16 seismic data to deduce reservoir permeability, (2) development of the geochemical inversion strategy and implementation of associated software, and (3) completing the software implementation of TProGS and the geostatistical analysis that provides the information needed when using the software to produce realizations of the Midale reservoir. The report partially the following deliverables: D2: Model development: MCMC tool (synthetic fluid chemistry data); deliverable completed. D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product; deliverable requirements partially fulfilled. D5: Field-based single-pattern simulations work product; deliverable requirements partially fulfilled. When completed, our completed stochastic inversion tool will explicitly integrate reactive transport modeling, facies-based geostatistical methods, and a novel stochastic inversion technique to optimize agreement between observed and predicted storage performance. Such optimization will be accomplished through stepwise refinement of: (1) the reservoir model - principally its permeability magnitude and heterogeneity - and (2) geochemical parameters - primarily key mineral volume fractions and kinetic data. We anticipate that these refinements will facilitate significantly improved history matching and forward modeling of CO{sub 2} storage. Our tool uses the Markov Chain Monte Carlo (MCMC) methodology. Deliverable D1, previously submitted as a report titled ''Development of a Stochastic Inversion Tool To Optimize Agreement Between The Observed And Predicted Seismic Response To CO{sub 2} Injection/Migration in the Weyburn-Midale Project'' (Ramirez et al., 2009), described the stochastic inversion approach that will identify reservoir models that optimize agreement between the observed and predicted seismic response. The software that implements this approach has been completed, tested, and used to process seismic data from pattern 16. A previously submitted report titled ''Model verification: synthetic single pattern simulations using seismic reflection data'', Ramirez et al. 2010, partially fulfilled deliverable D3 by summarizing verification activities that evaluate the performance of the seismic software and its ability to recover reservoir model permeabilities using synthetic seismic reflection data. A future progress report will similarly describe summarizing verification activities of the geochemical inversion software, thereby completing deliverable D3. This document includes a chapter that shows and discusses permeability models produced by seismic inversion that used seismic data from pattern 16 in Phase 1A. It partially fulfills deliverable D5: Field-based single-pattern simulations work product. The D5 work product is supposed to summarize the results of applying NUFT/MCMC to refine the reservoir model and geochemical parameters by optimizing observation/prediction agreement for the seismic/geochemical response to CO{sub 2} injection/migration within a single pattern of Phase 1A/1B. A future progress report will show inversion results for the same pattern using geochemical data, thereby completing deliverable D5. This document also contains a chapter that fulfills deliverable D2: Model development: MCMC tool (synthetic fluid chemistry data). The chapter will summarize model development activities required to facilitate application of NUFT/MCMC to optimize agreement between the observed and predicted geochemical response to CO{sub 2} injection/migration. Lastly, this document also contains a chapter that partially fulfills deliverable D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product. This work product is supposed to summarize model development activities required for (1) application of TProGS to Weyburn, (2) use of TProGS within the MCMC tool, and (3) application of the MCMC tool to address field seismic and geochemical data. The chapter included here fulfills requirements 1 and 2. Requirement 3 will be addressed in a future progress report.

Preface: Evolution of the deep South China Sea: Integrated IODP Expedition 349 results

Cenozoic tectonic evolution of offshore Chinese Basins and its response to geodynamic processes of the East Asian Continental Margin

Seismic reflection amplitude data are increasingly used in reservoir modeling to provide information on changes in earth properties away from well locations. In geostatistical reservoir modeling, the most common application is to use seismic data as background data in some form of comodeling. Seismic data image reflectors and not earth layer properties. Therefore, prior to use in comodeling, seismic data must first be transformed into an earth layer property. Typically, the transform is to acoustic impedance using an appropriate seismic inversion method. Seismic inversion methods generate results that are generally band limited in nature, resulting in limits to vertical resolution. The vertical resolution achieved can be an order of magnitude below the vertical model resolution required from geostatistical reservoir modeling, which is in the order of well-log resolution. Hence, in using seismic data, geostatistical modelers encounter a problem of downscaling, not the more commonly encountered upscaling problem. This difference in scale introduces scatter between the primary data with well-log order resolution and the secondary seismically derived rock property data used in the comodeling. As a result, to preserve vertical heterogeneity, only limited use of the secondary data can be made in comodeling procedures. This results in models that only partially fit the seismic data, i.e., only limited use is made of the seismic information. If the secondary data are more strongly imposed, the fit to the seismic data improves, but the required vertical heterogeneity is not preserved. The inability to overcome this difference in scale issue, therefore, limits the value of the application of comodeling methods to integrate seismic data into reservoir models. One class of geostatistical methods that overcomes this limitation relies on iterative geostatistical modeling. In these methods, referred to as geostatistical seismic inversion, the iterative modeling process is conditioned such that the final models generated closely match the seismic data while maintaining the required vertical heterogeneity. The application of these methods is computationally expensive relative to comodeling methods but is now practical for large models on today's desktop hardware. Relative to comodeling, geostatistical seismic inversion methods make full use of the information carried in the seismic data, resulting in a significant reduction in model uncertainty away from well control.

Subduction zones are among the most active tectonic areas on the planet. Their primary characteristic is the enormous amount of stress accumulated at the interface between the subducting oceanic plate and the overriding plate. The release of this stress is accommodated by a wide range of behaviours, ranging from aseismic slip (slip at speeds too slow to radiate seismic energy), through the spectrum of slow slip and tremor, to seismic slip capable of generating major earthquakes. The main investigative tools for subduction zones to map out this range of behaviour, and to assess the coupling properties of the subduction interface, involve the direct observation of ground movements through geodesy (either terrestrial or satellite-based) or through local seismic surveillance using near-field instrumentation, all of which are logistically complex, and typically only feasible on land.Utilizing the recent expansion of seismic arrays in continental regions, we propose an alternative approach for the study of subduction zones that bypasses the aforementioned limitations through the use of teleseismic waves—recorded at a distance between 30º and 90º from the epicenter—based on the identification of the presence (or absence) of highly reflective layers at the megathrust interface. Previous studies using local seismic data have observed the presence of highly reflective layers, characterized by strong impedance contrasts, located at the megathrust interface, capable of producing a reflection in the wavefield that results into the presence of precursors of depth phases. Since impedance contrasts in the solid Earth are linked to variations in the elastic properties of the medium, reflectivity offers a window into the rheology of the plate interface. Understanding the reasons behind such strong impedance contrasts, their potential variability over time and space, could pave the way for understanding why the degree of coupling of subduction interfaces varies, whether it is related to transient processes, or if it is stable over time.Here, we present an automated waveform processing approach designed to detect such reflections in remote seismic data, and illustrate this with a test region from the Central America subduction zone.  We analyse waveforms produced by seismic events with magnitudes ranging from 4.5 to 5.5 occurring at different times and recorded by small aperture seismic arrays. Our observations in Central America prove to be an excellent tool for studying the coupling properties of the megathrust interface. This work represents a first attempt, with the ultimate goal of mapping subduction zones and their coupling properties, even in currently inaccessible submarine areas, allowing for a better understanding of the seismic risk that subduction zones represent.

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three‐dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square (RMS) amplitude performed the best The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified: debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well‐logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies‐controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single‐well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

Modern three-dimensional (3D) seismic data assist not only in delineating reservoir geometry, but also in predicting porosity and lithology variations away from well control. This case study of an oil-producing channel sand in the Taber/Turin area, Alta., Canada illustrates the improvement in reservoir characterization achieved with an integrated approach incorporating both well and seismic information.

The understanding of the reservoir in the West Brae field in the North Sea has improved because of the incorporation of reprocessed seismic data into reservoir characterization and modeling. The field was discovered in 1975 with initial production in 1997 from two early Eocene turbidite sands in the Balder and Sele formations (Flugga sand member). Both turbidite sands are of good quality, with an average porosity of 30%, an average net-to-gross ratio of 85%, and permeability up to 7500 md. The field produces mainly black oil (22 API) with a dry gas cap and has two distinct oil-water contacts. A high-quality four-dimensional seismic data set was acquired in 2007, which was parallel processed with the 1993 baseline seismic data. These new data prompted a rebuild of the reservoir model to assess the potential for bypassed hydrocarbons. The West Brae model is the result of a multidisciplinary reservoir characterization study that has incorporated attributes from the 1993 reprocessed seismic survey into the static geologic model. The key to incorporating the three-dimensional seismic data into the reservoir model was an elastic simultaneous inversion attribute that clearly identified the good-quality reservoir sands. The integration of the new seismic data into the West Brae reservoir model has improved reservoir understanding by (1) providing a stratigraphic framework for the geomodel, (2) refining the depositional model, and (3) creating more consistency in the geostatistical distribution of reservoir properties in the model. Colocated cokriging of the well data and a “soft” seismic attribute volume (Poisson impedance) has helped reduce the uncertainty of sand distribution and the prediction of flow potential in the West Brae field. This case study has shown that using a multidisciplinary team (geophysics, geology, petrophysics, and reservoir engineering) and an integrated data set significantly reduces the uncertainty for a reservoir characterization study.

Summary This paper presents an innovative methodology to integrate prior geologic information, well-log data, seismic data, and production data into a consistent 3D reservoir model. Furthermore, the method is applied to a real channel reservoir from the African coast. The methodology relies on the probability-perturbation method (PPM). Perturbing probabilities rather than actual petrophysical properties guarantees that the conceptual geologic model is maintained and that any history-matching-related artifacts are avoided. Creating reservoir models that match all types of data are likely to have more prediction power than methods in which some data are not honored. The first part of the paper reviews the details of the PPM, and the next part of this paper describes the additional work that is required to history-match real reservoirs using this method. Then, a geological description of the reservoir case study is provided, and the procedure to build 3D reservoir models that are only conditioned to the static data is covered. Because of the character of the field, the channels are modeled with a multiple-point geostatistical method. The channel locations are perturbed in a manner such that the oil, water, and gas rates from the reservoir more accurately match the rates observed in the field. Two different geologic scenarios are used, and multiple history-matched models are generated for each scenario. The reservoir has been producing for approximately 5 years, but the models are matched only to the first 3 years of production. Afterward, to check predictive power, the matched models are run for the last 1½ years, and the results compare favorably with the field data. Introduction Reservoir models are constructed to better understand reservoir behavior and to better predict reservoir response. Economic decisions are often based on the predictions from reservoir models; therefore, such predictions need to be as accurate as possible. To achieve this goal, the reservoir model should honor all sources of data, including well-log, seismic, geologic information, and dynamic (production rate and pressure) data. Incorporating dynamic data into the reservoir model is generally known as history matching. History matching is difficult because it poses a nonlinear inverse problem in the sense that the relationship between the reservoir model parameters and the dynamic data is highly nonlinear and multiple solutions are avail- able. Therefore, history matching is often done with a trial-and-error method. In real-world applications of history matching, reservoir engineers manually modify an initial model provided by geoscientists until the production data are matched. The initial model is built based on geological and seismic data. While attempts are usually made to honor these other data as much as possible, often the history-matched models are unrealistic from a geological (and geophysical) point of view. For example, permeability is often altered to increase or decrease flow in areas where a mismatch is observed; however, the permeability alterations usually come in the form of box-shaped or pipe-shaped geometries centered around wells or between wells and tend to be devoid of any geologica. considerations. The primary focus lies in obtaining a history match.

Huangjinba shale gas field is located at the south edge of the Sichuan Basin. It has very complex structures, in situ stresses and natural fracture corridors in comparison to adjacent areas in the Sichuan Basin. In recent drilling campaigns, drilling risks have caused some wells to fail in reaching their planned total depth, eventually failing to deliver cost-effective gas production. In order to mitigate drilling risks, e.g. mud loss, collapse, stuck, hang up, gas kick, effective drilling risk prediction is an urgent challenge to address. Integrating quantitative drilling risk prediction methods with qualitative methods could increase the prediction accuracy and avoid or mitigate the drilling risk during the well deployment stage. In this project, multiple seismic attributes were used to predict natural fracture distributions which qualitatively indicated the locations where drilling risks were likely occur. Comprehensive geophysical characterization was performed to identify natural fracture zones and patterns, and their mechanisms were validated by analyzing regional geological and tectonic evolution. Image log data was then integrated into the natural fracture distribution prediction from seismic to build a DFN (Discrete Fracture Network). This combination of the DFN predicted from seismic data plus quantitative image log information allowed improved accuracy in the prediction of drilling risks. Following this, natural fracture stability was analyzed by building a 3D geomechanics model in order to predict drilling complex qualitatively. A full field 3D geomechanics model was built through integrating seismic, geological structure, log and core data. The 3D geomechanical model includes 3D anisotropic mechanical properties, 3D pore pressure, and the 3D in-situ stress field. Through leveraging measurements from an advanced sonic tool and core data, the anisotropy of the formation was captured at wellbores and propagated to 3D space guided by prestack seismic inversion data. 3D pore pressure prediction was conducted using seismic data and calibrated against pressure measurements, mud logging data, and flowback data. The discrete fracture network model, which represented multi-scale natural fracture systems, was integrated into the 3D geomechanical model during stress modeling to reflect the disturbance on the in-situ stress field by the presence of the natural fracture systems. From these models, a drilling map which quantitatively indicated the depth where drilling risk such as mud loss, gas kick, etc. occurred was created along the well trajectory. This paper presents the highlights and innovations in seismic multi-attributes analysis and full-field geomechanics modeling which integrate qualitative and quantitative methods for drilling risk prediction.

On the value of 3D seismic amplitude data to reduce uncertainty in the forecast of reservoir production

Characterization of reservoir heterogeneity is a necessary step in reservoir delineation, characterization, and modeling. Reservoir heterogeneity can be described deterministically, statistically, or with reservoir-forming geologic processes. Deterministic heterogeneity models are easy to build, but may have insufficient resolution and may not provide enough insights into the reservoir and its properties. The parameterization of stochastic heterogeneity models is nontrivial. Seismic data can be used to determine geostatistical parameters or to refine the geometry parameters used for Boolean modeling. The geostatistical parameters (ranges and orientations) are obtained from seismic heterogeneity attributes measuring the second-order statistics contained in small seismic datacubes. Seismic heterogeneity relates to acquisition and processing artifacts, structure, or stratigraphy and lithology. Seismic data could also be used as additional constraints in Boolean reservoir models which allows both construction of conforming reservoir models and optimization of the geometry parameters to reduce the misfit between model and observations. Reservoirs could also be built by reconstructing their formation and evolution based on mathematical descriptions of processes such as sediment erosion, transport and deposition, compaction, deformation, subsidence and uplift, etc. The process parameters, e.g., rates of sediment input and transport, compaction, or subsidence, could be estimated by inversion of seismic and geologic data.