Permeability estimation from time-lapse seismic data for updating the flow-simulation model

The key to increasing reservoir recovery is to provide accurate estimates of the permeable pathways (permeability, transmissibility) and the transmissibility of the barriers that control reservoir heterogeneity. The reservoir-engineering techniques (such as well testing, well logging and production data) supply the estimate of these properties in the reservoir region which is limited to well locations. Providing estimates of the permeability in the reservoir rocks located between the wells is the holy grail of reservoir engineering for history matching. Compared with all other engineering techniques, 4D seismic could play a unique role in providing the property of the reservoir at a good spatial coverage. In this thesis, the estimation of permeability, transmissibility, and the transmissibility multiplier, using 4D seismic, is addressed. First, current methodologies for permeability estimation were applied in synthetic and field examples. Based on the investigations performed, the permeability-estimation method was modified and adjusted to produce an improved result. Consequently, the estimates of permeability provided an introduction to the fast-track history-matching method. The proposed history-matching technique implies a simple and practical approach for quickly updating the simulation to improve the history-matching in the model. In following, the assessment of the uncertainties associated with the permeability estimation that involves using a variety of different attributes, using different time-lapse surveys, tuning effects and method assumptions, were performed. The uncertainties were tackled by addressing these issues; thus, the permeability result was further enhanced, and the uncertainty associated with the estimates was quantified. Next, the relationships between the quantitative estimates of connectivity and the 4D seismic signal were established. Two types of connectivity assessments using 4D seismic (hydraulic sand connectivity and barrier connectivity) were proposed, depending on the fact that 4D-seismic information is either pressure- or saturation-dominant. Accordingly, two types of attributes were introduced, the seismic connectivity attribute (SCA) and the Laplacian attribute. When applied to the Schiehallion field data, an interpretation approach is used to interpret pressure- and saturation-anomalies in frequent time-lapse seismic, using all available sources of data. Following this, a pressure-anomaly map is utilized for locating faults and compartments iii (using the Laplacian attribute), and a saturation-anomaly map is used to calculate the SCA. New approaches were chosen for estimating transmissibility and transmissibility multipliers, based on proposed attributes extracted from 4D seismic

How to Model Condensate Banking in a Simulation Model to Get Reliable Forecasts? Case Story of Elgin/Franklin

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Parametrization of stochastic inputs using generative adversarial networks with application in geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude

Field application of an interpretation method of downhole temperature and pressure data for detecting water entry in horizontal/highly inclined gas wells

In the oil and gas industry today, continuous wellbore data can be obtained with high precision. This accurate and reliable downhole data acquisition is made possible by advancements in permanent monitoring systems such as downhole pressure and temperature gauges and fiber optic sensors. The monitoring instruments are increasingly incorporated as part of the intelligent completion in oil wells where they provide bottomhole temperature, pressure and sometimes volumetric flow rate along the wellbore - offering the promise of revolutionary changes in the way these wells are operated. However, to fully realize the value of these intelligent completions, there is a need for a systematic data analysis process to interpret accurately and efficiently the raw data being acquired. This process will improve our understanding of the reservoir and production conditions and enable us make decisions for well control and well performance optimization. In this study, we evaluated the practical application of an interpretation model, developed in a previous research work, to field data. To achieve the objectives, we developed a simple and detailed analysis procedure and built Excel user interface for data entry, data update and data output, including diagnostic charts and graphs. By applying our interpretation procedure to the acquired field data we predicted temperature and pressure along the wellbore. Based on the predicted data, we used an inversion method to infer the flow profile - demonstrating how the monitored raw downhole temperature and pressure can be converted into useful knowledge of the phase flow profiles and fluid entry along the wellbore. Finally, we illustrated the sensitivity of reservoir parameters on accuracy of interpretation, and generated practical guidelines on how to initialize the inverse process. Field production logging data were used for validation and application purposes. From the analysis, we obtained the production profile along the wellbore; the fluid entry location i.e. the productive and non-productive locations along the wellbore; and identified the fluid type i.e. gas or water being produced along the wellbore. These results show that temperature and pressure profiles could provide sufficient information for fluid identity and inflow distribution in gas wells

Coarse graining equations for flow in porous media: a HaarWavelets and renormalization approach

Coarse graining of equations for flow in porous media is an important aspect in modelling permeable subsurface geological systems. In the study of hydrocarbon reservoirs as well as in hydrology, there is a need for reducing the size of the numerical models to make them computationally efficient, while preserving all the relevant information which is given at different scales. In the first part, a new renormalization method for upscaling permeability in Darcy’s equation based on Haar wavelets is presented, which differs from other wavelet based methods. The pressure field is expressed as a set of averages and differences, using a one level Haar wavelet transform matrix. Applying this transform to the finite difference discretized form of Darcy’s law, one can deduce which permeability values on the coarse scale would give rise to the average pressure field. Numerical simulations were performed to test this technique on homogeneous and heterogeneous systems. A generalization of the above method was developed designing a hierarchical transform matrix inspired by a full Haar wavelet transform, which allows us to describe pressure as an average and a set of progressively smaller scale differences. Using this transform the pressure solution can be performed at the required level of detail, allowing for different resolutions to be kept in different parts of the system. A natural extension of the methods is the application to two-phase flow. Upscaling mobility allows the saturation profile to be calculated on the fine or coarse scale while based on coarse pressure values. To conclude, an alternative approach to upscaling in multi-phase flow is to upscale the saturation equation itself. Taking its Laplace transform, this equation can be reduced to a simple eigenvalue problem. The wavelet upscaling method can now be applied to calculate the upscaled saturation profile, starting with fine scale velocity data

4D Seismic History Matching Incorporating Unsupervised Learning

The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques