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Al-Jenaibi, Faisal (Abu Dhabi National Oil Company (ADNOC)) | Salameh, Lutfi A. (Abu Dhabi National Oil Company (ADNOC)) | Recham, Reda (Abu Dhabi National Oil Company (ADNOC)) | Meziani, Said (Abu Dhabi National Oil Company (ADNOC)) | Al Badi, Bader Saif (Abu Dhabi National Oil Company (ADNOC)) | Adli, Mostapha (Abu Dhabi National Oil Company (ADNOC))
Abstract This paper addresses proven best practice in modeling workflow including procedure and Qa/Qc criteria, which have to be applied during simulation models construction. The main issues discussed in this paper are as follows: –Static model with acceptable petrophysical parameters distribution including and honoring log and model Swi derived data per well as the basis for reliable dynamic model with realistic predictive mode. –Dynamic model as management tool with reasonable history match quality as assurance for reliable predictive mode of wells, areas and reservoir performance. –Define and quantify the volume of fluids-in-place, movable oil, residual oil and volumetric sweep efficiency to assess the reservoir potential, rate sustainability and economic ultimate recovery. –Assess the associated risks to development plans under selected development schemes with water/gas flood, WAG, artificial lift (ESP or Gas lift) and other EOR methods. –Model prediction mode quality and impact on strategic development decisions. As the oil industry has long experience in simulation techniques supported by availability of super computers and advanced software, it is observed that there are still major gaps that are not bridged yet. This paper will highlight some of those gaps and propose effective and practical solution based on best practice and lessons learnt in modeling studies to ensure reliable reservoir simulation predictive mode capabilities. This paper also includes the main criteria and assurance elements which were used to define modeling procedures that would participate in enhancing model reliability, and how they could impact development optimization process of selected production scheme towards achieving maximum recovery. Summary of these elements is as follows: Static to Dynamic Models Transition Phase –Well-per-well Swi match of log and model derived data. Acceptable level and trend match by using representative Pc's based on rock types & petrophysical data, MICP's, Height functions or combination. –Stability test to ensure good equilibrium condition with fluids distribution. –Well-per-well RFT/MDT field data and model derived data match. Dynamic Model History Match –Well-per-well acceptable trend match of observed data can be reached through a cycle of iterative process between geology, static and dynamic models to improve match. –Matching parameters and Qa/Qc criteria will be discussed later in details including; oil, gas and water rates and cumulative production, BHCIP, BHFP, WHFP, WCT and GOR. Prediction Mode of Development Plan –Well-per-well acceptable trend match (Rate, Pressures, WCT & GOR). –In case of abnormal predictive trend, consider the following remedial action: Review field measured data for accuracy, screen data as justified. Review imposed model constraints at well, group and field levels. Investigate solution with iterative process including static and dynamic models based on geology.
Abstract In this paper a fast track reservoir modeling and analysis of the Lower Huron Shale in Eastern Kentucky is presented. Unlike conventional reservoir simulation and modeling which is a bottom up approach (geo-cellular model to history matching) this new approach starts by attempting to build a reservoir realization from well production history (Top to Bottom), augmented by core, well-log, well-test and seismic data in order to increase accuracy. This approach requires creation of a large spatial-temporal database that is efficiently handled with state of the art Artificial Intelligence and Data Mining techniques (AI & DM), and therefore it represents an elegant integration of reservoir engineering techniques with Artificial Intelligence and Data Mining. Advantages of this new technique are a) ease of development, b) limited data requirement (as compared to reservoir simulation), and c) speed of analysis. All of the 77 wells used in this study are completed in the Lower Huron Shale and are a part of the Big Sandy Gas field in Eastern Kentucky. Most of the wells have production profiles for more than twenty years. Porosity and thickness data was acquired from the available well logs, while permeability, natural fracture network properties, and fracture aperture data was acquired through a single well history matching process that uses the FRACGEN/NFFLOW simulator package. This technology, known as Top-Down Intelligent Reservoir Modeling, starts with performing conventional reservoir engineering analysis on individual wells such as decline curve analysis and volumetric reserves estimation. Statistical techniques along with information generated from the reservoir engineering analysis contribute to an extensive spatio-temporal database of reservoir behavior. The database is used to develop a cohesive model of the field using fuzzy pattern recognition or similar techniques. The reservoir model is calibrated (history matched) with production history from the most recently drilled wells. The calibrated model is then further used for field development strategies to improve and enhance gas recovery.
Mohmad, Nis Ilyani (Petronas Carigali Sdn. Bhd) | Mandal, Dipak (Petronas Carigali Sdn. Bhd) | Amat, Hadi (Petroliam Nasional Berhad) | Sabzabadi, Ali (Petroliam Nasional Berhad) | Masoudi, Rahim (Petroliam Nasional Berhad)
History Matching (HM) is one of the critical steps for dynamic reservoir modelling to establish a reliable predictive model. Numerous approaches have emerged over the decades to accomplish a robust history matched reservoir model ranging from the classical reservoir engineering approach to the widely accepted 3D numerical simulation approach and its variations. As geological complexity of the oil and gas field increases (multilayered reservoirs, heavily faulted) compounded with completion complexity (dual strings, commingle production), building a fully representative predictive reservoir model can be arduous to almost impossible task.
Artificial Intelligence (AI) and machine learning has advanced almost all major industries, including the petroleum industry in general and reservoir engineering. The objective of this paper is to outline a novel approach in history matching using a data-driven approach through Artificial Intelligence via Artificial Neural Network (ANN) and Data-Driven Analytics.
In this paper, a step by step methodology in building a reservoir model and history matching process using ANN will be described which includes data preparation and data QA/QC, spatiotemporal database formulation, reservoir model design, ANN architecture design, model training and history matching strategy. A case study of the implementation to Field "A" in Malaysian waters is presented where good to fair history matching quality was obtained for all production parameters. Field "A" is a 25kmx75km oil sandstone reservoirs of a highly geologically complex field (more than 200 major and minor faults, more than 30 reservoir layers) of more than 25 years of production. The challenges of history matching of this field does not only lie on its geologically complex structure and its corresponding subsurface uncertainties, but also on the production strategy of the wells that involved commingled dual strings production with several integrity issues that adds additional dimensions to the field's complexities. To date, Field "A" has no field wide history matched reservoir model using conventional numerical simulation method available due to the complexity of history matching. This long history matching woe is mitigated via the implementation of AI based reservoir model and Data Analytics. This novel approach is estimated to be more time and cost-efficient compared to the conventional method.
The comparison of this AI based reservoir model and history matching methodology with the conventional numerical reservoir model approach will be discussed. Furthermore, the advantages, limitations and areas of improvements of this AI based history matching methodology will also be highlighted.
The target audience of this paper would be to reservoir engineering practitioners and dynamic model simulators who is interested to learn the complementary or alternative approach in reservoir modelling apart from conventional numerical modelling in order to create time-efficient reservoir model and reducing the risks in their field development plans.
Masoudi, Rahim (Petronas MPM) | Mohaghegh, Shahab D. (West Virginia University) | Yingling, Daniel (Intelligent Solutions, Inc.) | Ansari, Amir (Intelligent Solutions, Inc.) | Amat, Hadi (Petronas MPM) | Mohamad, Nis (Petronas COE) | Sabzabadi, Ali (Petronas MPM) | Mandel, Dipak (Petronas COE)
Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time.
The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process.
Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented.
A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 143875, "Modeling, History Matching, Forecasting, and Analysis of Shale-Reservoir Performance Using Artificial Intelligence," by Shahab D. Mohaghegh, SPE, Intelligent Solutions and West Virginia University, and Ognjen Grujic, SPE, Seed Zargari, SPE, and Masoud Kalantari, West Virginia University, prepared for the 2011 SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, 19-21 April. The paper has not been peer reviewed.
Advances in horizontal drilling and multistage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexity of the flow mechanism in the natural fracture and its coupling with the matrix and the induced fracture, the effect of geomechanical parameters, and optimum design of hydraulic fractures has not necessarily kept up with our interest in these prolific hydrocarbon-rich formations. A new approach to modeling, history matching, forecasting, and analyzing oil and gas production in shale reservoirs was developed. It uses pattern-recognition capabilities of artificial intelligence and data mining as a workflow to build a full-field reservoir model to forecast and analyze oil and gas production from shale formations.
A reservoir-simulation and -modeling technology called top-down intelligent reservoir modeling [referred to here as top-down modeling (TDM)] was applied to shale formations, and the full-length paper details examples for New Albany, Lower Huron, and Bakken shales. Natural fractures in the shale have the highest permeability in the reservoir and, as the main conduit, contribute significantly to production. Multistage hydraulic-fracturing procedures enable reaching and intersecting the existing natural fractures in the shale formation. Mapping of the natural fractures in the shale formations has proved to be an elusive task. Even with the most-advanced logging technologies, only the intersection of the natural fractures with the wellbore can be detected, while the extent of these fractures beyond the wellbore and how they are distributed throughout the reservoir (between wells) remain the subject of research.
TDM tries to model the effects of hydraulic and natural fractures on the production from wells rather than modeling the discrete fracture networks themselves. While the development of stochastic realizations of natural fractures and their intersection with the induced hydraulic fracturing is being studied with stochastic and numerical reservoir modeling, TDM fills the existing gap for a predictive model that can be built by use of a minimum number of assumptions about the nature of the reservoir and about our understanding of its complexity. TDM starts with a solid assumption that whatever the nature of the natural-fracture distribution and its interaction with the induced hydraulic fractures may be, these factors must influence the amount of the hydrocarbon that each well is able to produce. These signatures can be used to build reservoir models, match the production history, and build a predictive model that can aid reservoir-management decisions.