Karst systems heterogeneity may become a nightmare for reservoir modelers in predicting presence, spatial distribution, impact on formation petrophysical characteristics, and particularly in dynamic behaviour prediction. Moreover, the very high resolution required to describe in detail the phenomena does not reconcile with the geo-cellular model resolution typically used for reservoir simulation. The scope of the work is to present an effective approach to predict karst presence and model it dynamically. Karst presence recognition started from the analysis of anomalous well behaviour and potential sources of precursors (logs, drilling evidence, etc.) to derive concepts for karst reservoir model. This first demanding step implies then characterizing each cell classified as karstified in terms of petrophysical parameters. In a two-phase flow, karst brings to fast travelling of water which leaves the matrix almost unswept. This feature was characterized through dedicated fine simulations, leading to an upscaling of relative permeability curves for a single porosity formulation. The workflow was applied to a carbonate giant field with a long production history under waterflood development. Firstly, a machine learning algorithm was trained to recognize karst features based on log response, seismic attributes, and well dynamic evidence, then a karst probability volume was generated and utilized to predict the karst presence in the field. Karst characterization just in terms of porosity and permeability is sufficient to model the reservoir when still in single phase, however it fails to reproduce observed water production. Karst provides a high permeability path for water transport: classical history match approaches, such as the introduction of permeability multipliers, proved to be ineffective in reproducing the water breakthrough timing and growth rate. In fact, the reservoir consists of two systems, matrix, and karst: however, the karst is less known and laboratory analysis shows relative permeability only for the matrix medium. The introduction of equivalent or pseudo-relative permeability curves, accounting for both the media, was crucial for correct modelling of the reservoir underlying dynamics, allowing a proper reproduction of water breakthrough timing and water cut (WCT) trends. The implementation of a dedicated pseudo relative permeability curve dedicated to karstified cells allowed to replicate early water arrival, thus bringing to a correct prediction of oil and water rates, also highlighting the presence of bypassed oil associated with water circuiting, particularly in presence of highly karstified cells.
Karachaganak field is one of the largest accumulations of gas-condensate in the world. Located in the northern Pre-Caspian Basin (Kazakhstan), the field is a Permo-Carboniferous isolated carbonate platform with a hydrocarbon column of about 1500 m. In production since 1985, the actual development focuses the oil rim with gas injection implemented, since 2004, in a confined area of the Platform Interior. Various future development scenarios are now being considered to more fully develop the reservoir, and some of the recovery processes being modeled require an improved understanding of the internal reservoir architecture. In fact the internal reservoir architecture is rather complex, affected by the initial development of aggrading mounds followed by progradation (consisting most likely in a clinoform geometry) passing to cyclic, grain-dominated platform interior sediments. The resulting reservoir quality is quite heterogeneous with low porosity but locally high productivity when affected by micro-fractures and vugs. In this context a deep analysis has been performed considering the location of additional injection in different areas/regions of the field evaluating the possible risks and the uncertainties affecting the liquid recoveries. Nine different areas characterised by specific geological/dynamic behaviour have been investigated. Moreover considering the Prograding area, alternative models were built in order to address the possible impact of Clinoforms on the flow patterns. The final analysis, which takes into consideration the possible liquid recovery and the relevant knowledge and complexity of the different areas, provided an improved view to optimize the future injection. In fact, the Cyclic Platform, where injection has been already implemented, appears to be the best candidate; other areas, although affected by a certain degree of uncertainty, also seem promising from the recovery point of view, while some other regions, characterised by high compartmentalisation, appear to be less interesting.
Reservoir studies, including preparation of field development plan, are processes typically dominated by time constraints. In general, reservoir studies consist in multiple geoscience activities integrated to build a fine geological model that eventually leads to an upscaled numerical model suitable for history matching and forecast simulations. In the simulation stage, the quality and effectiveness of the activity is highly dependent on the computational efficiency of the numerical model. This is particularly true for complex, supergiant carbonate reservoirs. Often, even with today's simulators, upscaling is still needed and simplifications can be implemented to allow computationally intensive history matching and risk analysis workflows. This paper provides some real field examples where these issues were faced and successfully managed, without applying further simplifications to the geological concept of the model: principles of reservoir simulations and common sense reservoir engineering were used to adjust properties of the model and then speed-up numerical simulation. Tuning included a combination of various solutions, such as deactivating critical cells whenever possible, calibrating convergence and time stepping control, tweaking field management to prevent instability in the computation, optimization of number of cores and cells split among cores to optimize load balancing and scalability. These solutions were used on two super-giant carbonate fields, a triple porosity (matrix, karst and fractures) undersaturated light oil reservoir and a supercritical gas and condensate reservoir. The former field was described using an upscaled model of about 700,000 active cells and a dual porosity - dual permeability formulation; the latter was described by a relatively coarse model of about 400 thousand active cells using a single porosity formulation. Large speed-up, up to five times with respect to reference simulations, was achieved without simplifying the geology and losing accuracy perceivably. Benefits were achieved for both conventional and high-resolution simulators.
Karachaganak Field, a Permo-Carboniferous carbonate platform, is a retrograde gas-condensate-oil reservoir located in the Pricaspian Basin. The field, in production since 1985, holds about 9 Bbls of oil and gas condensate and 48 Tcf of natural gas. Historically, the reservoir properties distribution has been debated because of both the complex depositional setting and the strong diagenetic overprint. These uncertainties have been assessed in the present study by analyzing and integrating the vast amount of geological and production data with the target of building a history matched reservoir model. Seismic facies analysis, integrated with analogues outcrop and core and log data, reveals different geological contexts that ranges from platform interior bedded deposits to aggrading mounds, prograding clinoforms, slopes and basin sediments. Different Depositional Regions, characterized by specific petrophysical characteristics, as estimated from core, log, Well-Tests, PLT and production data, were defined. The study, performed through an iterative process between geological and numerical models, allowed the definition of geologically meaningful interpretations of the complex dynamic behaviors. Different critical issues were better addressed by Depositional Region: quantity and magnitude of the enhanced permeability related to micro-fracturing and vugs, rock matrix permeability scale factors from plug to whole core, geological meaning and impact of sealing barriers, dolomite estimation and modeling, mound size range and modeling. A "best estimate" reference model has been thus defined and a very good history match achieved. The uncertainties of the field have been then investigated considering the achieved history match as the benchmark. Starting from the reference model the petrophysical characteristics of the different Depositional Regions have been changed far from the history matched wells at an estimated threshold distance not perturbing the history match. Low and high scenarios models were thusly defined. These represent possible alternative "end members" consistent with the geological data and still endorsed by the history match.
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