Dynamic Reservoir Data Research Articles

Main Controlling Factors and Prediction Model of Fracture Scale in Tight Sandstone: Insights from Dynamic Reservoir Data and Geomechanical Model Analysis

Main Controlling Factors and Prediction Model of Fracture Scale in Tight Sandstone: Insights from Dynamic Reservoir Data and Geomechanical Model Analysis

An Overview of the Differential Carbonate Reservoir Characteristic and Exploitation Challenge in the Tarim Basin (NW China)

The largest marine carbonate oilfield and gas condensate field in China have been found in the Ordovician limestones in the central Tarim Basin. They are defined as large “layered” reef-shoal and karstic reservoirs. However, low and/or unstable oil/gas production has been a big challenge for effective exploitation in ultra-deep (>6000 m) reservoirs for more than 20 years. Together with the static and dynamic reservoir data, we have a review of the unconventional characteristics of the oil/gas fields in that: (1) the large area tight matrix reservoir (porosity less than 5%, permeability less than 0.2 mD) superimposed with localized fracture-cave reservoir (porosity > 5%, permeability > 2 mD); (2) complicated fluid distribution and unstable production without uniform oil/gas/water interface in an oil/gas field; (3) about 30% wells in fractured reservoirs support more than 80% production; (4) high production decline rate is over 20% per year with low recovery ratio. These data suggest that the “sweet spot” of the fractured reservoir rather than the matrix reservoir is the major drilling target for ultra-deep reservoir development. In the ultra-deep pre-Mesozoic reservoirs, further advances in horizontal drilling and large multiple fracturing techniques are needed for the economic exploitation of the matrix reservoirs, and seismic quantitative descriptions and horizontal drilling techniques across the fault zones are needed for oil/gas efficient development from the deeply fractured reservoirs.

Seismic identification of carbonate reservoir sweet spots using unsupervised machine learning: A case study from Brazil deep water Aptian pre-salt data

The pre-salt high-yield carbonate reservoirs of Brazil are responsible for about 75% of national oil production. Still, seventeen years since its discovery, detailed seismic characterization of these reservoirs remains challenging, with producing and nonproducing lithologies often exhibiting the same amplitude response. Those reservoirs are complex, composed of high productivity lacustrine carbonates (stromatolites and grainstones), mixed with non-producing mudstones, clays, and igneous intrusions. To address this heterogeneity, we evaluate unsupervised machine learning clustering techniques including Self Organizing Maps (SOM), Generative Topographic Maps (GTM), and K-means and apply them to a suite of instantaneous, geometric, interval velocity, and AVO seismic attributes. We use both static and dynamic reservoir data from 13 wells at the Mero field that penetrated lacustrine carbonates from Barra Velha formation to validate the accuracy of our unsupervised learning models. These wells are excellent blind tests as they were not used for any algorithm training. In this pioneer work, the first done using machine learning in the Mero field, we find that SOM, GTM, and K-means models provide clusters that can be linked to sweet spots associated with fracturing, karstification, and hydrothermal alteration. Additionally, clusters related to sill intrusions, platform facies and distal fine grained non-reservoirs were identified. The results can be extrapolated to the adjacent Central Libra Area (still in exploration phase), where we have identified the same clusters of the sweet spots reservoirs in Mero Field and, thus, they can be considered as future targets. For accuracy assessment, the same attribute combination SOM clustering was performed in another Petrobras’ pre-salt confidential dataset and the results were satisfactory. So, for exploration projects optimization purposes, the new proposed workflows can also be applied to other areas with geological similarity to the Mero Field.

Characterization and analysis of naturally fractured gas reservoirs based on stimulated reservoir volume and petro-physical parameters

Fracture is one of the most important geological phenomena that affect the production of hydrocarbon compounds in broken carbonate reservoirs. However, fracture controlling factors must be combined with well data to achieve accurate fracture modeling. Therefore, structural data, drilling data, well flow diagrams, cores data, wells production data, and dynamic reservoir data have been considered here. Finally, by combining the above-mentioned information and through statistical and mathematical methods, the mechanism of fracture creation, general trends, and dominant fracture patterns are determined. These patterns are directly related to the tectonic regime and the stresses governing the region. For the first time, in this paper, we divided Zubair carbonate gas reservoir into 10 zones based on porosity and water saturation, and shale volume variation. We conclude that just four-zone of these are economic producible. Besides, the dominant lithology of this formation is more than limestone and a small number of thin shale layers. We defined types of cross-sectional petro-physical graphs and confirmed them by the geological graphic diagram prepared at the head.

History matching and uncertainty quantification for velocity dependent relative permeability parameters in a gas condensate reservoir

In gas condensate reservoirs, gas flow at large velocities enhances the gas permeability due to gas-liquid positive coupling which results in near-miscible flow condition. On the other hand, augmented pressure drop due to non-Darcy flow, reduces the gas permeability. Models for the “Positive Coupling” or “non-Darcy flow” include several parameters, which are rarely known from reliable lab special core analysis. We offer a good alternative for tuning of these parameters in which the observed production history data are reproduced from the readjusted simulation model. In this study, history matching on observed production data was carried out using evolutionary optimization algorithms including genetic algorithms, neighborhood algorithm, differential evolution algorithm and particle swarm optimization algorithm, where a faster convergence and lower misfit value were obtained from a genetic algorithm. Then, the “Neighborhood Algorithm–Bayes” was used to perform Bayesian posterior inference on the history matched models and create the posterior cumulative probability distributions for all uncertain parameters. Finally, Bayesian credible intervals for production rate and wellhead pressure were computed in the long-range forecast. Our new approach enables to not only calibrate the gas effective permeability parameters to dynamic reservoir data, but allows to capture the uncertainty with parameter estimation and production forecast.

A deep learning approach to anomaly detection in geological carbon sequestration sites using pressure measurements

Carbon capture and storage (CCS) has been extensively investigated as a potential engineering measure to reduce anthropogenic carbon emission to the atmosphere. Real-time monitoring of the safety and integrity of carbon storage reservoirs is a critical aspect of any commercial-scale CCS deployment. Pressure-based sensing is cost effective, suitable for real-time monitoring, and scalable to large monitoring networks. However, questions remain on how to best harness intelligent information from the high-frequency pressure monitoring sensors to support real-time decisions. This work presents a deep-learning-based framework for analyzing and detecting anomalies in pressure data streams by using a convolutional long short-term memory (ConvLSTM) neural network model, which allows for the fusion of both static and dynamic reservoir data. In ConvLSTM, the convolutional neural network (CNN) is used for spatial pattern mining and the LSTM is used for temporal pattern recognition. The performance of the ConvLSTM model for real-time anomaly detection is demonstrated using a set of pressure monitoring data collected from Cranfield, Mississippi, an active enhanced-oil-recovery field. The anomaly detection model is trained using bottom-hole pressure data acquired from the base experiment (without leak event) and then tested on pressure data collected during a series of controlled CO2 release experiments (with artificially created leak events). Results show that the ConvLSTM neural network model successfully detected anomalies in the pressure time series obtained from the controlled release experiments. Inclusion of static information into the model further improves the robustness of ConvLSTM.

Integration of static and dynamic reservoir data to optimize the generation of subsurface fracture map

This paper presents a new three-dimensional fully coupled poroelastic numerical model to simulate pressure transient response of naturally fractured reservoirs. One of the main applications of the new approach is to improve the reservoir characterization by decreasing the uncertainties associated with subsurface fracture map and to understand the interaction between fracture and matrix. The model is based on a hybrid methodology to simulate fluid flow by combining a single continuum and discrete fracture network approaches. To decrease the uncertainty associated with subsurface fracture map, a different realizations of the discrete fracture systems are generated based on image logs, seismic, geological features and the tectonic history of the studied reservoir. An iterative loops have been used between the reservoir static model and dynamic well-test data to optimize the generation of subsurface fracture maps. At each loop, a new discrete fracture system is used and the simulated pressure transient data are compared with the available field data. The process is repeated till the matching between the simulated and the measured pressure transient data is achieved using the most appropriate fracture realization. The numerical model is validated against an analytical pressure transient solution for dual-porosity system, and then applied to a real case taken from fractured basement reservoir in offshore Southern Vietnam. The developed hybrid methodology used to simulate fluid flow and rock deformation to improve the characterization of the fractured basement by simulating the pressure transient testing. The results predicted by the presented model are in a good agreement with field data; also the model was able to predict the fractures network distribution and fractures inherent properties around the tested well.

Reservoir description of a mid-Cretaceous siliciclastic-carbonate ramp reservoir: Mauddud Formation in the Raudhatain and Sabiriyah fields, North Kuwait

ABSTRACTThe mid-Cretaceous Mauddud Formation is the main producing carbonate reservoir in the Raudhatain and Sabiriyah fields of northern Kuwait. Historical field information and results from waterflood pilots indicate that reservoir performance in these reservoirs is controlled by geological complexity at several scales. A detailed, integrated sedimentological and biostratigraphic investigation of the reservoirs, combined with dynamic reservoir data, have provided an understanding of Mauddud reservoir heterogeneity and of the principle controls on reservoir matrix behaviour. The largely carbonate Mauddud Formation overlies the Upper Burgan Member, a thick succession of fluvio-deltaic deposits, and consists of a diverse suite of carbonate facies deposited in low to high-energy, shallow-marine ramp settings. The basal part of the reservoir comprises mixed carbonate and siliciclastic sediments and reflects the establishment of a carbonate-dominated regime during waning supply of Burgan siliciclastic sediment. This system was eventually drowned and covered by the Wara Formation, a shaly offshore succession that is also the reservoir seal.Sedimentary facies associations and microfossil assemblages within the reservoir are organised in a broadly upward-shallowing succession constructed of several transgressive-regressive cycles, which are defined by prominent, widely-correlatable flooding surfaces. Each cycle exhibits a characteristic internal stacking pattern of minor depositional cycles. Field-wide mapping and interpretation of facies within each cycle reveals a SW to NE, proximal to distal, trend consistent with regional seismic and palaeogeographic interpretations. The high-energy, inner to mid-ramp carbonate succession in the lower portion of the Mauddud reservoir is punctuated by siliciclastic incursions. Abrupt lateral facies changes, thickness variations, and local intra-reservoir erosion surfaces in this section suggest that deposition was influenced by subtle syndepositional tectonism. The upper part of the reservoir, in contrast, lacks significant siliciclastic influence and is made up of widely-correlatable, lower-energy carbonate facies, although local subtle facies variations show that the Raudhatain-Sabiriyah structures continued as palaeohighs during deposition. The contrast in quality between grain-dominated facies at the crests of the two structures and less grainy facies along their flanks was accentuated by carbonate cementation in the water legs of the reservoirs, largely in the form of calcite concretions of variable abundance. Cementation is most pronounced in low-energy wackestone facies, particularly in proximity to flooding surfaces where nodules may be amalgamated to form laterally continuous, cemented layers which are commonly fractured. Another significant, but contrasting, diagenetic modification within the reservoir was the generation of secondary macroporosity through dissolution of aragonitic skeletal components in a shallow to intermediate burial environment.The stratigraphic evolution of the Mauddud reservoir, and its diagenetic overprint, in addition to post-depositional fracturing and faulting, created reservoir heterogeneities, which are critical to reservoir performance; one of the most significant of these is the relationship between horizontal and vertical permeability. Parasequences dominated by high-energy inner ramp grainstones, thin inner ramp rudist-bearing tempestites, and vuggy and fractured rudist floatstones and rudstones constitute thief zones that represent major challenges to reservoir management. In contrast, some cemented layers and flooding surfaces support pressure differentials of up to several hundred pounds/square inch (psi), thus complicating sweep and promoting reservoir compartmentalisation. The strong facies, diagenetic and stratigraphic controls on the distribution of thief zones and intra-reservoir baffles demonstrates how important it is to comprehensively understand reservoir sedimentology and stratigraphy when devising long-term development plans for reservoirs of this deceptively simple character.More recent 3-D seismic data, production surveillance, and horizontal development wells show faults and fractures to be important heterogeneities in both reservoirs. Due to the immaturity of the water flood in the Mauddud reservoirs, the impact of these features on field and well behaviour is as yet unclear, but it is anticipated that the impact of such features on well and field performance will become more pronounced during later development.

Integration of 3D seismic data and dynamic reservoir data for exploitation of remaining oil in a mature field: A case study in western China

Time-lapse (4D) seismic is a tried and tested method to monitor fluid changes in a producing reservoir, but its effectiveness and reliability depend on the quality and repeatability of the surveys. It still has significant limitations when applied on land for monitoring some deep reservoirs (depth >1000 m) where production rates are low and many other factors are unfavorable for effective 4D acquisition, processing, and interpretation. Modern high-density, wide-azimuth, and multi-component data-acquisition techniques have resulted in a step change in reservoir imaging and fluid prediction; however, the absence of a baseline or equivalent data set hinders the utility of such good data for inferring reservoir changes and locating remaining or bypassed hydrocarbons.

Dynamic Reservoir Data Assimilation With an Efficient, Dimension-Reduced Kalman Filter

Summary Kalman filter-based methods have been widely applied for assimilating new measurements to continuously update the estimate of state variables, such as reservoir properties and responses. The standard Kalman filtering scheme requires computing and storing the covariance matrix of state variables, which is computationally expensive for large-scale problems with millions of gridblocks. In the ensemble Kalman filter (EnKF), this problem is alleviated with sampling from a limited number of realizations and computing the required subset of the covariance matrix at each update. However, the goodness of the (ensemble) covariance approximated from the limited ensemble depends on the number of realizations used and the representativity of a given ensemble. In this study, we propose an efficient, dimension-reduced Kalman filtering scheme based on Karhunen-Loeve (KL) and other orthogonal polynomial decompositions of the state variables. We consider flow in heterogeneous reservoirs with spatially variable permeability. The reservoir responses such as pressure are measured at some locations at various time intervals. The aim is to dynamically characterize the reservoir properties and to predict the reservoir performance and its uncertainty at future times. In our scheme, the covariance of the reservoir properties is approximated by a small set of eigenvalues and eigenfunctions using the KL decomposition and the reconstruction of the covariance from the KL decomposition can be done whenever needed. In each update, the forecast step is solved using the KL-based moment method, giving a set of functions from which the mean and covariance of the state variables can be constructed, when needed. The statistics of both the reservoir properties and the reservoir responses are then updated with the available measurements at this time using the auto-and cross-covariances obtained from the forecast step. The new approach is illustrated on a heterogeneous reservoir with dynamic measurements and the results are compared with those from the EnKF method, in terms of accuracy and efficiency.

Takula Field: Data Acquisition, Interpretation, and Integration for improved Simulation and Reservoir Management

Summary This paper discusses the integration of dynamic reservoir data at the flow-unit scale into the reservoir management and reservoir simulation efforts of the Takula field. The Takula field is currently the most prolific oil field in the Republic of Angola.

Dynamic Reservoir Data: Part II – Interpretation, Averaging and Use of the Basic Geological-Engineering Data

Abstract and Introduction This paper is the second half and an integral part of a previous presentation entitled: " Interpretation, Averaging and Use of the Basic Geological-Engineering Data, STATIC RESERVOIR DATA (Part I)," presented at the 17th Annual Technical Meeting of The Petroleum Society of CIM, in Edmonton, Alberta, in May 1966; published in JCPT, October-December, 1966 and republished by SPE of AIME as Paper SPE 1974 (1967). The purpose of these two complemental papers is to emphasize that taking, assembling and averaging of the basic data for further use in an analysis of a reservoir requires a profound knowledge of many of the specialized disciplines of modern geology and petroleum engineering. " DYNAMIC RESERVOIR DATA (Part II)," presented in this particular paper, investigates the level of energy, the potential and also the forces contained or induced into the reservoir. These aspects are specifically discussed under the following major subheadings:Pressures to be Used in Analysis of a Reservoir:datum,depth of measurements,original reservoir pressure,bubble point pressure,pressures taken during the production life-averaging,superposition theorem,variable pressures-stepwise curve, and,incremental vs partial pressure drops.PVT Data: (a) sampling, (b) laboratory results, and (e) adjustment to different pb.Resume on Dynamic DataBibliographic References