This paper presents an application of integrated asset modeling (IAM) to a rich gas condensate field under recycling mode located in Abu Dhabi. The field is composed of many non communicating gas reservoir units; some of these units are already developed and being produced for a number of years, while some others reservoirs are in the exploration / evaluation phase. Potentially, some of the reservoir units are sharing or will share the surface network and the process facilities. The project consists of developing a platform for a solution that can respond to the current requirement of the available modeled reservoirs; at the same time, the solution should be expandable to account for the reservoirs being explored or at early production phase. The first step of the study was to construct the surface network for both gas injectors and gas producers. The subsurface compositional simulation models for the developed reservoirs were available and history matched. The platform linking the surface to subsurface was developed and set to fulfill the field development requirements. The solution was validated for the historical performances, measurement of the surface network were collected and validated in the stand-alone mode and in the coupling mode. Tests for the prediction performances were also performed, and led to more realistic profile showing the recoverable reserves for recycling and blowdown considered scenarios. The integrated model indicated area of improvement in pressure history match of the field simulation model for few wells where it was not easy to observe in standalone simulations. The platform for the integrated Asset modeling is expandable to further development that could be plugged-in, either functional adds-on like process modeling and economic evaluation or organic like adding additional wells to the existing models or adding new models for exploration unit. It will be also applicable to see the compression requirements during any time in the future.
In this paper we introduce for the first time an innovative approach for deriving Oil Formation Volume Factor (Bo) by mean of artificial intelligence method. In a new proposed application Self-Organizing Map (SOM) technology has been merged with statistical prediction methods integrating in a single step dimensionality reduction, extraction of input data structure pattern and prediction of formation volume factor Bo. The SOM neural network method applies an unsupervised training algorithm combined with back propagation neural network BPNN to subdivide the entire set of PVT input into different patterns identifying a set of data that have something in common and run individual MLFF ANN models for each specific PVT cluster and computing Bo. PVT data for more than two hundred oil samples (total of 804 data points) were collected from the north African region representing different basin and covering a greater geographical area were used in this study. To establish clear Bound on the accuracy of Bo determination several statistical parameters and terminology included in the presentation of the result from SOM-Neural Network solution. the main outcome is the reduction of error obtained by the new proposed competitive Learning Structure integration of SOM and MLFF ANN to less than 1 % compared to other method. however also investigated in this work five independents means of model driven and data driven approach for estimating Bo theses are 1) Optimal Transformations for Multiple Regression as introduced by (McCain, 1998) using alternating conditional expectations (ACE) for selecting multiple regression transformations 2), Genetic programing and heuristic modeling using Symbolic Regression (SR) and cross validation for model automatic tuning 3) Optimization by successive alteration of the empirical constant of the correlation parametric form of Standing correlation or any other, 4) Correlation refinement using Rafa Labedi approach tuning existing working correlation of Standing and others for calculating Bo by adjusting constant in the linear trend component of the formula structure of Standing correlation using a subset of the original correlation called correlation variable of formation volume factor this type of correlation refinement was extended and applied to modify any other available correlation to predict Bo 5) Machine learning predictive model (Nearest Neighbor Regression, Kernel Ridge regression, Gaussian Process Regression (GPR), Random Forest Regression (RF), Support Vector Regression (SVM), Decision Tree Regression (DT), Gradient Boosting Machine Regression (GBM), Group modeling data handling (GMDH). Regression Model Accuracy Metrics (Average absolute relative error, R-square), diagnostic plot was used to address the more adequate techniques and model for predicting Bo.
This paper presents the application of an integrated modeling approach to the facility design and construction stages of a mega-project for a giant oilfield offshore Abu Dhabi. The scale of the EPC task is unprecedented in the UAE and requires careful design to optimize the capital investment. In addition, the project uncertainties require that a high degree of flexibility be factored into the design process. The integrated modeling approach couples surface and subsurface flow models to achieve a complete system solution that incorporates many levels of constraints and realistically represents future behavior. This approach addresses a number of key issues. Firstly, multiple different quality reservoirs produce to a shared surface facility. Consequently, the field is highly sensitive to back pressure variation and so requires a rigorous treatment of well and surface physics. Secondly, the sub-surface uncertainties and sheer size of the investment requires a flexible approach to design, hence, many simulation scenarios are required to provide improved decision support. Finally, close collaboration is required between the sub-surface and surface teams to ensure optimization of facilities design and reservoir management for cost and recovery. The adopted methodology utilizes an integration framework which couples reservoir and topsides models into a predictive tool for development planning. This paper describes how the integrated modeling approach was utilized to provide input to design process for several aspects of the field development plan during the design and construction stages. This will include discussion of the phasing of the production facilities, requirement for temporary facilities, modular compression and separation units and the optimization of the drilling program for planned infill wells. The paper presents a best integrated modeling practice supporting facility design process which is applicable for similar scale projects, highlighting the role of integrated model as a means to foster collaboration between surface and sub-surface teams.
This paper presents an application of integrated asset modeling to a giant offshore oil field. The field is located northwest of Abu Dhabi Island and is one of the largest offshore fields in the world. The asset comprises several individually modeled reservoir layers sharing a common surface facility. The traditional method of modeling this field involves running separate simulation models assuming fixed boundary conditions at the wellhead. This does not accurately model the effects of the constraints imposed by the surface facility. The primary aim of this paper is to highlight the importance of integrated asset modeling in formulating an optimized, cost- effective development plan. This is achieved through the provision of realistic production profiles, taking into account the impact of system backpressure and changes in operating conditions. Secondly, integrated modeling acts to reduce uncertainty in the design data in terms of phased production for future facility upgrading and replacement. Finally, integrated modeling provides a framework for production system optimization under different development schemes. Included in the discussions presented here are a validation of the integrated asset modeling tool, an overview of the business requirements for the operation of the field over the next 30 years, and analysis of selected development strategies highlighting the added value of integrated asset modeling. The results of the integrated studies helped to formulate decisions on infill drilling based on realistic production profiles. Secondly, they served to reduce risk through better understanding of the surface and subsurface interaction. Thirdly, they helped to support the decision for commissioning a new concept facility layout (artificial islands), which represents a significantly lower CAPEX investment with more flexibility. Finally, the integrated study assisted in making decisions on the application and type of artificial lift and displacement mechanisms. Introduction The exploration and production of hydrocarbons encompasses numerous scientific and engineering disciplines including geosciences, reservoir, production, and facilities engineering. It is widely recognized that integration of these discipline silos is of key importance in optimizing the profitability of an oilfield asset. Information technology is a key enabler in each engineering domain. Numerous workflow and simulation software products have evolved over the past 30 years that have led to significant productivity and recovery improvements. Complex reservoirs are modeled more easily, multiphase flow is simulated, and performance and sizing of equipment may be optimized. This paper aims to highlight the importance of integrated asset modeling in the FEED and field development planning processes of a giant offshore oil field. The asset under consideration here is one of the largest of its type in the world covering an areal extent of 1,200 square kilometers. There are three distinct reservoir strata producing through approximately 450 single and dual string wells. The three major reservoirs, Reservoirs I, II, and III, are geologically characterized as multiple carbonate layers separated by impermeable strata. Each reservoir has its own layering scheme with different vertical and lateral permeability distribution and thickness. The northeast area is generally highly faulted and fractured. These geological features result in different well productivities and completion types. The field was initially developed with a peripheral waterflood strategy. Subsequently, five spot pattern and staggered line drive water injection schemes were introduced. Ongoing field development planning studies call for intensive infill drilling and application of artificial lift and/or Enhanced Oil Recovery (EOR) to different reservoir areas.
Sharjah National Oil Corporation (SNOC) operates three onshore reservoirs in the Emirate of Sharjah. The reservoir simulation models use compositional modelling to capture the fluid dynamics in mature, low porosity highly fractured gas condensate fields. The scope of this project was to improve the reservoir characterization by investigating and overcoming lack of water production in compositional models for effective EOR and gas storage strategies. Water cut of 30%+ comprised of a combination of produced and condensed water in a reservoir with no active aquifer, thus posing a modelling challenge combined with a lack of comprehensive historical PVT data. All existing PVT reports in the database were retrieved and a comprehensive quality check was performed. The best possible PVT results for each field were short-listed and taken as reference datasets for validating the compositional EoS in a depleted field. A new EOS was generated for these fields based on legacy PVT data combined with 38+ years of production data. A shortfall of this new EOS was the inability to produce condensed water as observed in the field with Chloride counts less than 1500 ppm. To rectify this low water production mismatch, a blind test was conducted introducing water as a component in the EoS in the simulation model to see the effect. Moreover, extensive scale problems in any of the wells of 30-year-old mature assets leading to regular interventions never occurred in the asset's operational history. As expected, mobility of the fluids in the system had changed and low salinity condensed water was seen to have a good match. Liberated water was traced at the surface to confirm water production rate of the same order of magnitude as observed in production data. Due to overwhelming water production rates from the trial test, SNOC decided to perform a comprehensive extended PVT study. The naturally fractured carbonates were subjected to geological and material balance study and the data indicated an absence of active aquifers, which made it difficult to match observed water production in simulation models. To effectively plan future EOR projects like gas storage, it was necessary to model the effects of water and its interaction with injected fluids in the reservoir while honouring low water movement in the subsurface. The paper provides a novel workflow for generation of the compositional equation of state with water as a component in retrograde condensate fields. The workflow followed the lumping of hydrocarbon components to minimise runtime and capture maximum possible fluid dynamics in the reservoir without compromising the fluid properties observed in the PVT lab. It was also vital for the simulation model to honour the production history spanning over three decades. It also highlights the ability and importance of including water as an EOS component to effectively capture the condensed water in the reservoirs that many works of literature and simulators are unable to provide insight on.
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