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Scott L Montgomery - One of the best experts on this subject based on the ideXlab platform.

  • permian clear fork group north robertson unit integrated reservoir management and characterization for Infill Drilling part ii petrophysical and engineering data
    AAPG Bulletin, 1998
    Co-Authors: Scott L Montgomery, David K Davies, Richard K Vessell, James E Kamis, William H Dixon
    Abstract:

    Located in the Permian basin of west Texas, North Robertson unit (NRU) produces from highly heterogeneous carbonate reservoirs of the Permian (Leonardian) Clear Fork Group and lowermost Glorieta Formation. Discovered in 1955, the productive area encompasses 5366 ac (2173 ha) along the northern border of the Central Basin platform and contains an estimated 262 MMbbl original oil in place (OOIP). Total recovery from the unit, including 30 yr of primary production and 10 yr of waterflood production, is only 10.1% OOIP. A recent integrated reservoir characterization effort in NRU suggests that previous recovery efforts were inadequate to achieve maximum production from the reservoir. Analysis of petrophysical properties in the Clear Fork has allowed for pore-level modeling, leading to identifying rock types and delineating flow units. Maps constructed of various reservoir parameters, including OOIP, primary and secondary ultimate recovery, and flow capacity, were compared with rock-type distribution within individual flow units and the results of reservoir simulation to estimate portions of the unit suitable for targeted 10-ac (4-ha) Infill development. Related data also suggested the importance of completion techniques in helping improve conformity between producers and injection intervals in pay intervals in nearby wells. Results of 14 producing wells and 4 injector wells drilled on the basis of the new reservoir model and improved completion techniques were impressive and confirmed the value of using such an approach to optimize the economics and production of Infill development in such a complex reservoir.

  • permian clear fork group north robertson unit integrated reservoir management and characterization for Infill Drilling part i geologic analysis
    AAPG Bulletin, 1998
    Co-Authors: Scott L Montgomery
    Abstract:

    North Robertson unit (NRU) produces from highly heterogeneous, shallow-marine platform carbonate reservoirs of the Permian (Leonardian) Clear Fork and lowermost Glorieta formations. Discovered in 1955, the productive area encompasses 5366 ac (2173 ha) along the northern border of the Central Basin platform (CBP) in the Permian basin of west Texas. Clear Fork/Glorieta production is widespread in this portion of the basin, with most fields, including NRU, now under waterflood. Primary production was on 40 ac (16 ha) spacing, with enhanced recovery dependent upon a combination of 20 ac (8 ha) Infill Drilling and waterflooding. Total recoveries in these fields have been typically below 25%, with many waterfloods performing below prediction due to a high level of lateral and vertical reservoir heterogeneity. Detailed geological and petrophysical analysis of new and existing cores from NRU, performed as part of a combined industry-U.S. Department of Energy (DOE)-sponsored project, has resulted in greatly improved reservoir characterization of the Clear Fork productive interval, ultimately allowing for the quantification and mapping of specific productive intervals within the 1000 ft+ (300 m+) gross reservoir interval. Relevant data have been used to identify optimal sites for strategic 10 ac (4 ha) Infill development aimed at maximizing the efficiency and profitability of future oil-reserve growth within the unit. A total of 14 producing wells and 4 injector wells were drilled on the basis of the new geologic engineering model. Results of these wells were impressive and confirmed the value of targeted Infill development in such a complex reservoir. Part I of this article summarizes field data and geologic information essential to the reservoir characterization effort in NRU. Part II of this article, to appear in the November issue of the Bulletin, will discuss petrophysical and engineering data, as well as the targeted Infill Drilling effort and its results.

William H Dixon - One of the best experts on this subject based on the ideXlab platform.

  • permian clear fork group north robertson unit integrated reservoir management and characterization for Infill Drilling part ii petrophysical and engineering data
    AAPG Bulletin, 1998
    Co-Authors: Scott L Montgomery, David K Davies, Richard K Vessell, James E Kamis, William H Dixon
    Abstract:

    Located in the Permian basin of west Texas, North Robertson unit (NRU) produces from highly heterogeneous carbonate reservoirs of the Permian (Leonardian) Clear Fork Group and lowermost Glorieta Formation. Discovered in 1955, the productive area encompasses 5366 ac (2173 ha) along the northern border of the Central Basin platform and contains an estimated 262 MMbbl original oil in place (OOIP). Total recovery from the unit, including 30 yr of primary production and 10 yr of waterflood production, is only 10.1% OOIP. A recent integrated reservoir characterization effort in NRU suggests that previous recovery efforts were inadequate to achieve maximum production from the reservoir. Analysis of petrophysical properties in the Clear Fork has allowed for pore-level modeling, leading to identifying rock types and delineating flow units. Maps constructed of various reservoir parameters, including OOIP, primary and secondary ultimate recovery, and flow capacity, were compared with rock-type distribution within individual flow units and the results of reservoir simulation to estimate portions of the unit suitable for targeted 10-ac (4-ha) Infill development. Related data also suggested the importance of completion techniques in helping improve conformity between producers and injection intervals in pay intervals in nearby wells. Results of 14 producing wells and 4 injector wells drilled on the basis of the new reservoir model and improved completion techniques were impressive and confirmed the value of using such an approach to optimize the economics and production of Infill development in such a complex reservoir.

Roussos Dimitrakopoulos - One of the best experts on this subject based on the ideXlab platform.

  • optimizing Infill Drilling decisions using multi armed bandits application in a long term multi element stockpile
    Mathematical Geosciences, 2018
    Co-Authors: Rein Dirkx, Roussos Dimitrakopoulos
    Abstract:

    Mining operations face a decision regarding additional Drilling several times during their lifetime. The two questions that always arise upon making this decision are whether more Drilling is required and, if so, where the additional drill holes should be located. The method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. The MAB optimizes the best Infill Drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. The proposed method is applied to a long-term, multi-element stockpile, which is a part of a gold mining complex. The stockpiles in this mining complex are of particular interest due to difficult-to-meet blending requirements. In several mining periods grade targets of deleterious elements at the processing plant can only be met by using high amounts of stockpiled material. The best pattern is defined in terms of causing the most material type changes for the blocks in the stockpile. Material type changes are the driver for changes in the extraction sequence, which ultimately defines the value of a mining operation. The results of the proposed method demonstrate its practical aspects and its effectiveness towards the optimization of Infill Drilling schemes.

  • optimizing Infill Drilling decisions using multi armed bandits application in a long term multi element stockpile
    Les Cahiers du GERAD, 2017
    Co-Authors: Rein Dirkx, Roussos Dimitrakopoulos
    Abstract:

    Every mining operation faces a decision regarding additional Drilling at some point during its lifetime. The two questions that always arise upon making this decision are whether more Drilling is required and, if so, where the additional drill holes should be located. The method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. The MAB optimizes for the best Infill Drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. MAB formulations are commonly used in many applications where decisions have to be made between different alternatives with stochastic outcomes, such as Internet advertising, clinical trials and others. The application of the proposed method to a long-term, multi-element stockpile, which is a part of a gold mining complex in Nevada, USA, demonstrates its practical aspects.

A M Bubela - One of the best experts on this subject based on the ideXlab platform.

  • non parametric regression and neural network Infill Drilling recovery models for carbonate reservoirs
    Computers & Geosciences, 2000
    Co-Authors: R D Soto, Peter P Valko, A M Bubela
    Abstract:

    This work introduces non-parametric regression and neural network models for forecasting the Infill Drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas. Development of the oil recovery forecast models helps understand the relative importance of dominant reservoir characteristics and operations variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological settings, and make operations decisions. The variety of applications demands the creation of multiple recovery forecast models. One of the significant constraints for the model development is the limited number of field data that are inexact and often exhibit uncertain relationships. The inexact and uncertain relationship may also encompass a large number of possible independent variables. This situation mandates proper selection of independent variables for the Infill Drilling recovery model. Non-parametric regression and multivariate principal component analysis are used to identify the dominant and the optimum number of independent variables. The advantage of the non-parametric regression is easy to use and can quickly provide results that reveal the dominant independent variables and relative characteristics of the relationships. The disadvantage is retaining a large variance of forecast results for a particular data set. The insight of interdependency of the variables gained in non-parametric regression and multivariate principal component analysis is employed to develop an eAective neural network. The neural network Infill Drilling recovery model is capable of forecasting the oil recovery with less error variance. This work shows that a multiple use of various modeling techniques may provide a healthy interaction between the diAerent approaches and thereby, a better oil recovery forecast. 7 2000 Elsevier Science Ltd. All rights reserved.

  • development of Infill Drilling recovery models for carbonate reservoirs using neural networks and multivariate statistical as a novel method
    Ciencia Tecnologia y Futuro, 1999
    Co-Authors: Rodolfo Soto, A M Bubela
    Abstract:

    This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the Infill Drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (Infill Drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), Oilfield Intelligence (Ol), as an engineering tool to improve the characterization of oil and gas reservoirs. Ol integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial waterflooding, number of wells for primary recovery, number of Infill wells over the initial waterflooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery models. The neural network Infill Drilling recovery model is capable of forecasting the oil recovery with less error variance compared with non-parametric, fuzzy logic and regression models.

D A Bodnar - One of the best experts on this subject based on the ideXlab platform.

  • faulting a major control on fluid flow and production performance prudhoe bay field alaska
    AAPG Bulletin, 1991
    Co-Authors: D A Bodnar
    Abstract:

    During 13 years of development at Prudhoe Bay, several wells have been drilled near, or inadvertently through, faults, often resulting in impaired production performance. This paper provides examples of fluid movement through and along faults using open and cased hole logs and production data. The crest of the Prudhoe structure is relatively unfaulted. In contrast, peripheral areas are densely faulted, with large-throw northwest-southeast normal faults, complexity linked east-west and north-south trends. Most faults are transmissible where reservoir juxtaposes reservoir. Data from recently drilled wells show pressure communication across major faults. Highly transmissible fault planes allow rapid movement of water or gas to the perforated intervals of production wells. Faulting is the major route for aquifer influx through the bottom-sealing tar layer. Fault-related gas influx from the expanding gas cap has caused diminished oil production, as well as shut in at high gas-oil ratios. Faults provide vertical communication for injected gas and water to perforations in the oil column. Rapid waterflood breakthrough along faults from injectors to producers bypasses oil in places. Lost circulation, adverse hole conditions causing poor wireline logs, and inadequate primary cementing may result from Drilling through fault zones. These problems result in increased wellwork, lost production, andmore » occasionally, the need for redrills, all of which increases cost. The majority of wells are unfaulted in the reservoir section, but with continued Infill Drilling, targeting new wells to avoid faults becomes increasingly difficult. The importance of accurate structural mapping tied to well data, therefore, cannot be overstated.« less