Distribution Of Reservoir Parameters Research Articles

Research on the Construction Method of a Training Image Library Based on cDCGAN

There is a close relationship between the size and property of a reservoir and the production and capacity. Therefore, in the process of oil and gas field exploration and development, it is of great importance to study the macro distribution of oil–gas reservoirs, the inner structure, the distribution of reservoir parameters, and the dynamic variation of reservoir characteristics. A reservoir model is an important bridge between first-hand geologic data and other results such as ground stress models and fracture models, and the quality of the model can influence the evaluation of the sweet spots, the deployment of a horizontal well, and the optimization of the well network. Reservoir facies modeling and physical parameter modeling are the key points in reservoir characterization and modeling. Deep learning, as an artificial intelligence method, has been shown to be a powerful tool in many fields, such as data fusion, feature extraction, pattern recognition, and nonlinear fitting. Thus, deep learning can be used to characterize the reservoir features in 3D space. In recent years, there have been increasing attempts to apply deep learning in the oil and gas industry, and many scholars have made attempts in logging interpretation, seismic processing and interpretation, geological modeling, and petroleum engineering. Traditional training image construction methods have drawbacks such as low construction efficiency and limited types of sedimentary facies. For this purpose, some of the problems of the current reservoir facies modeling are solved in this paper. This study constructs a method that can quickly generate multiple types of sedimentary facies training images based on deep learning. Based on the features and merits of all kinds of deep learning methods, this paper makes some improvements and optimizations to the conventional reservoir facies modeling. The main outcomes of this thesis are as follows: (a) the construction of a training image library for reservoir facies modeling is realized. (b) the concept model of the typical sedimentary facies domain is used as a key constraint in the training image library. In order to construct a conditional convolutional adversarial network model, One-Hot and Distributed Representation is used to label the dataset. (c) The method is verified and tested with typical sedimentary facies types such as fluvial and delta. The results show that this method can generate six kinds of non-homogeneous and homogeneous training images that are almost identical to the target sedimentary facies in terms of generation quality. In terms of generating result formats, compared to the cDCGAN training image generation method, traditional methods took 31.5 and 9 times longer. In terms of generating result formats, cDCGAN can generate more formats than traditional methods. Furthermore, the method can store and rapidly generate the training image library of the typical sedimentary facies model of various types and styles in terms of generation efficiency.

3D Spatial Distribution of Reservoir Parameters for Prospect Identification in ''Bizzy'' Field, Niger Delta

This study focuses on the use of some geostatistical tools to spatially distribute reservoir parameters in order to identify the bypassed prospects from the earlier seismic interpretation that was carried out in the field using 3D seismic data. Four wells and seismic data were used to generate the interpreted input horizon grids, fault polygons and to carry out detailed petrophysical analysis. Structural and property modeling which include; facies, net to gross, porosity and water saturation were distributed stochastically within the constructed 3D grid using Sequential Gaussian Simulation (SGS) algorithm. The reservoir structural model show system of different oriented growth faults F1 to F9. Faults F1, F2, F3 and F4 were the major growth faults, dipping towards south-west and are quite extensive almost across all the seismic section. A rollover anticline formed as a result of deformation of the sediments deposited on the downthrown block of fault F1. The other faults were minor fault (synthetic and antithetic). The trapping mechanism is a fault assisted anticlinal closure. Results from well log analysis and petrophysical models shows Godwin reservoir to be a moderate to good reservoir in terms of facies, with good net to gross, porosity, permeability and low water saturation. This study has also demonstrated the effectiveness of 3D geostatical modeling technique as a tool for better understanding the distribution with respect to space of continuous reservoir properties. It will also provide a framework for the future prediction of reservoir qualities and yield rate of the reservoirs.

Development of robust pressure management strategies for geologic CO2 sequestration

Injecting CO2 into deep geologic formations for permanent storage can potentially lead to leakage or induced seismicity if the overpressures exceed the fracture gradient or fault re-activation pressure. Strategies that remove reservoir fluids before or after injection may reduce these risks. But, even extensively characterized reservoirs can have substantial gaps in characterization necessary for developing optimal deterministic or even probabilistic pressure management strategies. The characterization data may not provide well-defined bounds or distributions of reservoir parameters or conditions (permeability, fault locations, fracture gradient, fault reactivation pressure). To assess the impact of these uncertainties, we present an approach for evaluating alternative pressure management strategies based on their robustness of meeting project performance criteria. We quantify the robustness of alternative strategies against several criteria: (1) exceeding fault re-activation pressure, (2) failing to inject a desired quantity of CO2, (3) exceeding a maximum quantity of extracted brine, and (4) failing to reach a desired extraction efficiency. Our approach allows nuances of competing and complimentary criteria to be quantitatively evaluated in a manner and in a level of detail not possible with optimization approaches. We illustrate the fundamentals of the approach on a simple one-dimensional analytical example using the Thiem equation. We demonstrate the approach using a numerical flow and transport model with uncertain heterogeneous permeabilities using data and site characteristics from the Rock Springs Uplift Carbon Storage Site in southwestern Wyoming.

Regional parametric sensitivity analysis of Walloon Subgroup CSG production

Following two decades of intensive exploration, coal seam gas (CSG) production in the Surat Basin has begun to dramatically increase to meet the capacity of three newly completed CSG to liquefied natural gas (LNG) export projects. As the industry’s focus shifts from appraisal to exploitation, the production forecasts underpinning these LNG projects are being tested. In some cases predicted reservoir performance is found to be invalidated by observed production data, a condition that may require costly amendments to project schedule and scope. The deviation between actual and predicted reservoir performance can often be attributed to an incomplete understanding of parametric uncertainties present in static or dynamic reservoir models. To address this limitation, this study aims to explore the parametric controls upon CSG production behaviours with a series of simulation experiments. Distributions of reservoir parameters were compiled from 152 open-source well completion reports available in three areas along the eastern edge of the Surat Basin. These distributions were validated and then sampled to extract representative ranges for subsurface factors including gas content, permeability, net coal thickness, Langmuir pressure, Langmuir volume and drainage area. These inputs were used to construct single well radial models, which were then simulated to generate predictions of monthly and cumulative produced fluid volumes. The results of this study indicate that net coal thickness and lateral coal connectivity are the most sensitive factors with respect to cumulative gas production, while permeability was the single most influential parameter affecting the rate of gas production.

A stochastic modeling study of H56 block in Daqingzijing oilfield

After years of water injection, the underground movement of oil and water in Daqingzijing oilfield is becoming more and more complex, and the distribution of reservoir parameters between wells and remaining oil is difficult to predict. The reservoir geological model of H56 block in Daqingzijing oilfield is established with the support of numerous data by applying stochastic modeling techniques; it effectively reproduces the reservoir heterogeneity and distributes parameters in uncertain area between wells. A sequential indicator simulation algorithm is used to simulate the sedimentary facies, and facies-controlled reservoir characteristics have been performed including the reservoir petrophysical parameters and variability in the wells. In order to verify model accuracy, a comparison of porosity and permeability between model data and actual data is essentially made, the fitting of reserves and the original reserves were compared, and the whole field and the single well between the model and the history data were compared; the model shows consistency that gives confidence in using the model for numerical simulation purposes.

Integration of sequence stratigraphy and geostatistics in 3-D reservoir modeling: a case study of Otumara field, onshore Niger Delta

Sequence stratigraphic concepts for continental settings were assessed to analyze depositional systems of the formations penetrated by wells in the Otumara field. Identification and delineation of five sequences and their bounding surfaces was carried out using well logs. Reservoir sands A to D were mapped using conventional 3-D seismic interpretation techniques. Geostatistical simulation was carried out to provide equiprobable representations of the reservoirs, and the distribution of reservoir parameters and system tracts delineated from the stratigraphic framework. The modeled reservoir properties resulted in an improved description of reservoir distribution and connectivity. Reservoir sands A and B have the highest distribution of both highstand systems tract (HST) and lowstand systems tract (LST) deposits, while reservoir sands C and D have the lowest. Since reservoir sands C and D are from deeper depth, the results indicate that HST and LST decrease with depth while transgressive systems tract (TST) increases with depth. Correlating the 3-D geostatistical model with structures shows prospects with low and high hydrocarbon saturation. Crossplot of porosity and permeability for all reservoirs yielded good correlation. The crossplot of systems tract and hydrocarbon saturation with lithofacies as Z value shows a strong correlation of 0.89. The result also indicates that high hydrocarbon saturation is related to sandy facies of lowstand systems tract. Thus, the LST has the highest hydrocarbon potential. The models resulting from this study can be used to improve reservoir management and well placement, and to predict reservoir performance in Otumara field.

A multiresolution adjoint sensitivity analysis of time-lapse saturation maps

Improvements in seismic data acquisition and processing have made seismic technology a viable source of information for locating hydrocarbon deposits and also for describing the spatial variability of reservoir parameters. While three-dimensional seismic technology is already a well-established means of locating hydrocarbon deposits, the 4D (or time-lapse) seismic is gradually becoming a source of reservoir parameter description. In particular, time-lapse seismic is increasingly becoming a useful source of information about fluid migration and pressure changes in the reservoir. In recent years, time-lapse seismic data or saturation maps derived from such data have been used to estimate spatial distribution of reservoir parameters through history matching. However, successful application of the method to accurate description of reservoir parameter variability remains a challenge. Some major challenges in the application of time-lapse seismic data in reservoir model history match are the poor resolution of the seismic data, uncertainty associated with the maps of changes in saturation derived from the seismic data, and the massiveness of the data or the associated saturation maps. Repeat seismic data are blurred or low-resolution maps of trends in reservoir responses to production and injection activities. These trends are influenced by reservoir property distribution and when used judiciously, constitute a good source of information that can help in reducing the uncertainty associated with reservoir parameter estimates. The maps of changes in reservoir saturation or pressure obtained from repeat seismic data are themselves fraught with uncertainties and care must be taken when using these maps to estimate reservoir parameters. Furthermore, obtaining the most useful information from a voluminous seismic data set is challenging and is still an active area of research. In this work, we present the use of the wavelet transform to integrate maps of changes in reservoir saturation derived from time-lapse seismic data into reservoir model history matching. The work involves transforming the saturation-change map into a wavelet space and then history matching some carefully selected wavelets to obtain estimates of reservoir parameters. Three sample applications based on synthetic data are used to show the suitability of the approach. Comparison is made to conventional approach in which the saturation-change map is not transformed into wavelets before matching.

3D Microfacies Modeling of Tight Gas Reservoir: A Case from Changling Block, Jilin Oilfield

Reservoir geological model is actually used to characterize the reservoir structure and the spatial distribution of reservoir parameters and variations, thus, the prediction of reservoir parameters between wells is the core of geological modeling. In order to improve the accuracy of geological modeling, traditional cycle correlation and sequence stratigraphy method are combined to classfy the sub zones into single sand sections according to the sedimentary characteristics of Changling gas field. The study area has experienced many years of development. On one hand, researchers has a certain understanding of its underground geological characteristics. On the other hand, the well pattern is almost perfect, only the edge of study area needs a certain prediction. Therefore, deterministic and stochastic modeling methods are combined in microfacies modeling and a 3D regional microfacies model of Changling gas field is established finally.

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

Summary With recent advances in permanent downhole technology, well-test analysts now have to deal with enormous amounts of data. Using these data requires the development of efficient algorithms that are able to extract the relevant information from the data at minimal cost. We present a multiresolution wavelet approach to estimate the spatial distribution of reservoir parameters, by performing the nonlinear least-squares regression in the wavelet domains of both time and space. Wavelet transforms have the ability to reveal important events in time signals or spatial images. Thus, we transformed both the model space and the time-series pressure data into spatial wavelet and time wavelet domains, respectively, and used a thresholding to select a subset of wavelet coefficients from each of the transformed domains. These subsets were used subsequently in nonlinear regression to estimate the appropriate description of reservoir parameters. The appropriate subset is not only smaller; the problem is also reduced to the consideration of only the important components of the measured data and only the part of the reservoir description that depends on them. As a test of the approach, we first applied the model to well-test problems involving 1D (radially composite) reservoir systems. The inverse problem was solved to estimate the distributed permeability values by performing the nonlinear least-squares regression in the wavelet domains (time and space). Results obtained were compared with those obtained from the conventional nonlinear-regression approach, using all the pressure-time data and the full set of spatial reservoir parameters. The time/space wavelet approach proved to be efficient. By reducing the dimensions of the model and data spaces, the approach eliminates redundancy in the reservoir description and in the data set. Significantly, the approach reveals the true number of reservoir parameters that can be appropriately estimated from a given data set and also reveals which components of the full data set are active in constraining the reservoir model. Thus, the approach provides a good means to integrate different data properly while avoiding the inclusion of irrelevant data during nonlinear regression.

Reservoir Characterization from Insufficient Static Data Using Gradual Deformation Method with Ensemble Kalman Filter

Reservoir characterization is critical in order to estimate reserves and optimize oil and gas production. Ensemble Kalman filter characterizes the spatial distribution of reservoir parameters using covariances between static and dynamic data. Ensemble Kalman filter can rapidly provide results reflecting its overall tendency of parameter distribution, but may not characterize them in detail because ensemble Kalman filter does not minimize an objective function. Gradual deformation method is one of the well known non-gradient-based methods, which guarantees a global minimum theoretically. However, if permeability samples available do not represent the whole distribution, it can be hard for gradual deformation method to reduce the objective function sufficiently. In this study, results of ensemble Kalman filter are utilized as input permeability fields in gradual deformation method so that the demerit of each method is compensated by the merit. Although samples from a reservoir have no information on extreme permeability values, the developed gradual deformation method model with ensemble Kalman filter reliably characterizes hidden extreme values.

Point McIntyre Field, Alaska: An Integrated Reservoir Study

The geologic model of the Kuparuk Formation at Point McIntyre Field is the result of an integrated reservoir study. Data from 7000 feet of evenly distributed cores are the foundation for a lithofacies model, chronostratigraphic correlations, 2-D and 3-D mapping, and the geostatistical distribution of reservoir parameters. The resulting cellular model provides improved input to the simulator model used for reservoir management. The depositional lithofacies model is divided into 14 categories. These range from bioturbated, very fine-grained sandstone to conglomerates with abundant lithic mudstone clasts. Depositional lithofacies control reservoir quality, with the highest potential in massive or trough cross-bedded sandstones. The reservoir sandstones represent the marine portion of a fan-delta. No shales thicker than one inch are preserved within the Kuparuk Formation in the Point McIntyre cores. Accurate correlation and mapping of chronostratigraphic surfaces is essential to preserve porosity and permeability trends within the model. Sequence stratigraphic correlations show a complex depositional history and define twenty-one mappable zones with five major truncation surfaces. New zonal mapping methodologies honor the chronostratigraphy. Isochore maps contain strong depositional trends that influence reservoir management. A 3-D cellular model captures onlap and truncation relationships within the zones, preserving internal geometries. Lithofacies distributed within the 3-Dmore » model form a template tied to core and well control. The lithofacies template guides the geostatistical distribution of reservoir parameters.« less