Integration of time-lapse seismic data into dynamic reservoir model is an efficient process in calibrating reservoir parameters update. The choice of the metric which will measure the misfit between observed data and simulated model has a considerable effect on the history matching process, and then on the optimal ensemble model acquired. History matching using 4D seismic and production data simultaneously is still a challenge due to the nature of the two different type of data (time-series and maps or volumes based).
Conventionally, the formulation used for the misfit is least square, which is widely used for production data matching. Distance measurement based objective functions designed for 4D image comparison have been explored in recent years and has been proven to be reliable. This study explores history matching process by introducing a merged objective function, between the production and the 4D seismic data. The proposed approach in this paper is to make comparable this two type of data (well and seismic) in a unique objective function, which will be optimised, avoiding by then the question of weights. An adaptive evolutionary optimisation algorithm has been used for the history matching loop. Local and global reservoir parameters are perturbed in this process, which include porosity, permeability, net-to-gross, and fault transmissibility.
This production and seismic history matching has been applied on a UKCS field, it shows that a acceptalbe production data matching is achieved while honouring saturation information obtained from 4D seismic surveys.
Time-lapse changes in the overburden can be related to pore pressure variations in the underlying reservoir. The geomechanical changes observed are independent of fluid flow given the impermeable nature of the caprock, however, such deformation has the potential of causing a significant impact on the 4D signal. A physical model widely used to couple geomechanics and time-lapse seismic signatures, relates the fractional change in velocity and the vertical strain of reservoir and surrounding rocks via a constant factor
Presentation Date: Wednesday, October 17, 2018
Start Time: 1:50:00 PM
Location: Poster Station 17
Presentation Type: Poster
A review and analysis of post-stack time-lapse time-shifts has been carried out that covers published literature supplemented by in-house datasets available to the authors. From these data conclusions are drawn regarding the effectiveness of post-stack time-shifts for overburden and reservoir monitoring. Time-shift data are classified into those originating from geomechanical effects and those due to fluid saturation changes. A variety of field examples are shown that display the range and magnitude of variation in each class. The underlying physical mechanisms creating these time-shifts are then described, and linked to a series of generic and field-specific rock physics calculations that accurately predict their magnitudes. Conclusions are drawn regarding the reliability of this attribute for monitoring purposes, and the extent to which further research development is required.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 204C (Anaheim Convention Center)
Presentation Type: Oral
Dynamic changes in the overburden such as geomechanical variation and hydrocarbon migration which occur between the acquisition of 4D surveys influence reservoir monitoring programs and are typically not compensated for with current industry time-lapse workflows. We utilize prestack time-shift analysis for the Central North Sea Shearwater field to interpret observed dynamic overburden signatures in these attributes and propose a relation to 4D anisotropy due to production related stretching and extension. An inversion of the pre-stack time-shift data delineates velocity perturbations in the overburden associated with structural extension effects.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 204C (Anaheim Convention Center)
Presentation Type: Oral
In this paper we propose a proxy model based seismic history matching (SHM), and apply it to time-lapse (4D) seismic data from a Norwegian Sea field. A stable proxy model is developed for generating 4D seismic attributes by using only the original baseline seismic data and dynamic pressure and saturation predictions from reservoir flow simulation. This method (
In this study we firstly perform a check on the validity and accuracy of the proxy approach following the methodology of (
We propose a stable and accurate proxy for generating maps of 4D seismic attributes using only the original baseline seismic data and fluid-flow simulation predictions. The approach provides a fast track procedure for generating 4D seismic data from the simulator. It has particular use in quantitative 4D seismic analysis, and specifically for incorporating time-lapse seismic data into the history-matching loop where many seismic modeling iterations are required. The method circumvents the petro-elastic model with its associated uncertainties and also the need to choose a seismic full-wave or convolutional modeling solution. Despite the relative simplicity of the proxy, it is found not to bias the choice of optimal solution for the history match. Application to synthetic datasets based on a two North Sea fields indicates that the proxy can remain accurate to within a mean error of 5%.
Presentation Date: Monday, October 17, 2016
Start Time: 2:15:00 PM
Location: Lobby D/C
Presentation Type: POSTER
A comparative study is carried out between a deterministic and a stochastic approach via Bayesian McMC to obtain estimates of changes in pressure and saturation. The aim is to provide insights into well performance and pressure distribution within a geo-mechanically active chalk reservoir (Ekofisk). Uncertainty of such predictions is usually high; henceforth the solution of such an inverse problem is not limited to a single set of predicted parameters but represented by a probability density function on the model space. Both inversion approaches are similarly constrained by reservoir engineering concepts and predictions. We show that the Bayesian framework provides a suitable platform to incorporate data uncertainties and prior information. Quantitative interpretation on this field using the inversion results shows good agreement with well production data and helps to explain strong localized anomalies in both the Ekofisk and Tor formations.
Time-lapse (4D) seismic has now become commonplace in oil and gas field development. One branch of active research is the evolution of quantitative estimation of pressure and saturation changes from 4D seismic signals. Such dynamic properties have important implications in reservoir characterization such as optimizing well production and injection rate, placement of new wells, and the prevention of mechanical failures. The method presented here not only yields such properties but can also assist applications such as seismic history matching and model updating. However, one needs to ensure that a forward model can adequately describe time lapse elastic properties as a function of the dynamic reservoir parameters, and that the inverted dynamic properties are realistic and engineering consistent (EC).
In this study, an inversion was performed on the 4D geophysical parameters: relative changes in elastic properties and time shift measurements, into variations in pressure and saturation. The key difference between this inversion scheme and existing inversion methods lies in the characteristics of the reservoir itself. Ekofisk is a compacted chalk reservoir, which is not only subject to dry compaction but also water weakening; this behavior is taken into account in our forward model. Most importantly, the inversion is constrained with reservoir engineering predictions, which helps reduce the non-uniqueness involved and maintains consistency with the physics of flow.
The low but non-negligible permeability of shale enables pressure diffusion; the elastic implications of this process are sizeable over the production lifetime and can be recorded by time-lapse seismic monitoring (Ricard et al. 2012). To assess this effect our study include the internal architecture analysis of the shale in the 3D and 4D seismic modeling of two field applications: a shallow marine and a turbidite reservoir, where shale permeability, sand – shale geometry, time, reservoir connectivity and compartmentalization, determinate the impact of pressure diffusion in generating 4D signal related to changes in saturation (gas coming out of solution) and pressure.
Intrinsic properties have made shales regarded as impermeable rocks, and certainly conventional fluid flow through pure shales is almost negligible during hydrocarbon production time scale. This is the reason why they are not represented in reservoir simulation models (inactive cells). Classical reservoir characterization treats all shales as pure mudstones (almost 100% clay), when most intra and inter-reservoir shales are heterogeneous and anisotropic. Shale permeability, even when it’s in the order of few nanodarcys allow pressure diffusion, which impacts the production induced elastic changes in the reservoir recorded in time – lapse seismic monitoring (MacBeth et al. 2011, Ricard et al. 2012).
To evaluate pressure diffusion in shale and its effect on the reservoir 4D signal, the approach of this study is to include the internal sedimentological structure of the shale in the classical 3D and 4D seismic modeling workflow (figure 1). The start point is to recognize how the geological and simulation models represents shales and how these make them active, populating shale cells with static (NTG, porosity) and dynamic properties (permeability and transmissivity), estimated with a detailed geological analysis, upscaling and honoring the log data. Including lithological and fluid flow heterogeneity inside shales in the simulation model enables pressure and fluid interaction (flow almost null) with the reservoir. To analyze the effect of this process, simulation outputs and rock physics analysis applied to shale are integrated in synthetic seismic modeling.
This paper presents a history matching scheme that has been applied to production data and time lapse seismic data. The production data objective function is calculated using the conventional least squares method between the historical production data and simulation predictions, while the seismic objective function uses the Hamming distance between two binary images of the gas distribution (presence of gas (1) or absence of gas (0)) sequenced over the different acquisition times. The technique is applied to a UKCS (United Kingdom Continental Shelf) field that has deep-water tertiary turbidite sands and multiple stacked reservoirs defining some degree of compartmentalisation. Thirty five parameters are perturbed in this history match, they can be classified as volumetric parameters (net-to-gross, pore volume), transmissibility parameters (permeability, transmissibility), and end points of the relative permeability curves (critical saturation points). An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process. It is found that permeability and critical gas saturation are key parameters for achieving a good history match, and that the volumetric parameters are not significant for this match in this particular reservoir. We also observe that matching only to production data marginally improves the seismic match, whilst matching to only seismic data improves the fit to production data. Combining both sets of data delivers an improvement for the production data and seismic data, as well as an overall reduction in the uncertainties. A unique feature of this technique is the use of the Hamming distance metric for seismic data history matching analysis, as this circumvents the use of the uncertain petroelastic model. This approach is easy to implement, and also helps achieve an effective global history match.
Yin, Zhen (The Edinburgh Time-Lapse Project, Heriot-Watt University) | MacBeth, Colin (The Edinburgh Time-Lapse Project, Heriot-Watt University) | Chassagne, Romain (The Edinburgh Time-Lapse Project, Heriot-Watt University)
A technique is proposed to quantitatively measure interwell connectivity by correlating multiple 4D seismic monitors to historical well production data. We make use of multiple 4D seismic surveys shot over the same reservoir to generate an array of 4D seismic differences. Then a causative relationship is defined between the 4D seismic signals and changes of reservoir fluid volumes caused by injection and production behavior. This allows us to correlate seismic data directly to well data to generate a "well2seis" volume. It is found that the distribution of the "well2seis" correlation attributes reveals key reservoir connectivity features, such as the seal of faults, inter-reservoir shale and fluid flow pathways between wells, and can therefore enhance our interpretation on interwell connectivity. Combining with conventional interwell methods that are based on injection and production rate variations, this multiple 4D seismic method is found to support the conventional interwell approaches and can provide more reliable and detailed interpretation.
Our methodology is tested on a synthetic model extracted from full-field data for a Norwegian Sea reservoir, the fluid flow of which is controlled by fault compartmentalization and inter-reservoir shale. The full structural details and reservoir properties are preserved but three scenarios with different degrees of reservoir connectivity are created. It is found that proposed technique successfully detects the flow paths of the injected fluids in all reservoir scenarios. A volumetric attribute is created that accurately identifies the distinctive types of key flow barriers and conduits for each scenario that are known to be major factors influencing the reservoir dynamics. This proves that the well2seis attribute agrees with geological interpretations better than conventional well connectivity factors based on engineering data only. Additionally, the combination of the two types of methods provides a more robust tool for characterization of the reservoir connectivity by providing both quantitative degree and physical pattern of interwell communication.