The Effect Of Selective Producing Intervals On Secondary Recovery Efficiency In A Layered Reservoir

1965 ◽  
Author(s):  
Richard D. Seba
Keyword(s):  
Recovery Efficiency ◽  
Secondary Recovery

Quantification of Reservoir Connectivity for Reservoir Description Applications

1998 ◽  
Vol 1 (01) ◽  
pp. 12-17 ◽  
Author(s):  
K.B. Hird ◽  
Olivier Dubrule
Keyword(s):  
Cross Sections ◽  
Flow Simulation ◽  
Recovery Efficiency ◽  
Reservoir Properties ◽  
Flow Simulations ◽  
Reservoir Performance ◽  
Heterogeneous Reservoir ◽  
Water Breakthrough ◽  
Secondary Recovery ◽  
Reservoir Connectivity

Summary This study investigates means for efficiently estimating reservoir performance characteristics of heterogeneous reservoir descriptions with reservoir connectivity parameters. We use simulated primary and waterflood performance for two-dimensional (2D) vertical, two- and three-phase, black oil reservoir systems to identify and quantify spatial characteristics that control well performance. The reservoir connectivity parameters were found to correlate strongly with secondary recovery efficiency and drainable hydrocarbon pore volume. We developed methods for estimating primary recovery and water breakthrough time for a waterflood. We can achieve this estimation with three to five orders of magnitude less computational time than required for comparable flow simulations. Introduction Several geostatistical methods have been developed over the past decade for generating fine-scale, heterogeneous reservoir descriptions. These methods have become popular because of their ability to model heterogeneities, quantify uncertainties, and integrate various data types. However, the quality of results obtained with these stochastic methods is strongly dependent on the underlying assumed model. Reservoir heterogeneities will not be modeled correctly if the appropriate scales of heterogeneities are not considered. Uncertainties in future reservoir performance will not be quantified if the entire range of critical spatial characteristics are not explored. Simulated reservoir performance will not match historical performance if the appropriate data constraints are not imposed. The likelihood of using an inappropriate model can be greatly reduced if production data is integrated into the reservoir description process. This is because production data is influenced by those heterogeneities that impact future rates and recoveries. This paper investigates the applicability of using reservoir connectivity characteristics based on static reservoir properties as predictors of reservoir performance. We investigate two types of reservoir connectivity-based parameters. These connectivity parameters were developed to estimate secondary recovery efficiency and drainable hydrocarbon pore volume (HCPV). We use 2D vertical cross sections in the study. Previous work1–3 investigated the correlation of spatial reservoir parameters on reservoir performance for 2D areal reservoir descriptions. We first describe the general procedure. We then follow with definitions, more specific procedure details, and a discussion of the results for the two reservoir characteristics investigated. General Method We generated sets of permeability realizations, each set honoring at least the "conventional" geostatistical constraints (i.e., the univariate permeability distribution, the permeability variogram, and the wellblock permeabilities). We used simulated annealing4–6 to generate the permeability realizations and a linear porosity vs. log (permeability) relationship to obtain porosity values at each gridblock location. Porosity and permeability were the only heterogeneous reservoir properties considered during the study; reservoir thickness was assumed to be a constant. We performed all the flow simulations at the same scale as the permeability conditional simulations. The two- and three-phase black oil flow simulations were run with Amoco's in-house flow simulator, GCOMP,7 on a Sun SPARC 10 workstation.8 We used flow simulation results and analytical calculations to determine water breakthrough time (tBt) and ultimate primary oil recovery. The results for each flow simulation were plotted vs. values of various spatial permeability and porosity-based parameters. We identified the spatial parameter having the strongest correlation with each simulated performance data type. Recovery Efficiency Definitions. Secondary recovery efficiency is considered to be impacted by interwell reservoir connectivity characteristics. However, reservoir connectivity can be defined many different ways. A method has been reported that uses horizontal and vertical permeability thresholds to transform permeabilities to binary values.9 The least resistive paths are determined by finding the minimum distance required to move from one surface (i.e., a set of adjacent gridblocks) to another, for example, from an injector to a producer. We used a binary indicator approach to simplify the computations, thus resulting in an extremely fast connectivity algorithm. However, the success of the method is dependent on the applicability of the designated cutoff values. Such an approach would be most successful for systems comprised of two rock types (e.g., clean sand and shale), each having a small variance but significantly different means. The permeability distributions used in the present study do not fit in this category. Thus, attempts to correlate secondary recovery efficiency variables with the indicator-based connectivity parameters were unsuccessful. We concluded that a more sophisticated connectivity definition, accounting for actual permeability values, was needed to better quantify interwell reservoir connectivity. As a result of further investigation, the following connectivity parameter was developed for 2D cross sections: where IRe(i, k) is the secondary recovery efficiency "resistivity index" at gridblock (i, k), ?L is the distance between the centers of adjacent gridblocks, ka is the average absolute directional permeability between two adjacent gridblocks, krw(i) is the estimated relative permeability to water for the ith column, and A is the cross-sectional area perpendicular to the direction of movement. For a horizontal step, ?L/A=?Lx/?Lz, whereas for a vertical step, ?L/A=?Lz/?Lx . The resistivity index parameter is derived from the analogy between Darcy's law for linear, single-phase fluid flow, and Ohm's law for linear electric current where I is the electrical current, ?E is the voltage drop, and R is the electrical resistance. Inspection of Eqs. 2 and 3 shows that the permeance of the fluid system, kA/µL, is analogous to the reciprocal of the electrical resistance. Eq. 1 is the multiphase flow equivalent of the reciprocal of the permeance, dropping the viscosity constant µ.

scholarly journals Facile fabrication of ion-imprinted Fe3O4/carboxymethyl cellulose magnetic biosorbent: removal and recovery properties for trivalent La ions

RSC Advances ◽  
2021 ◽  
Vol 11 (41) ◽  
pp. 25258-25265
Author(s):  
Long Liu ◽  
Sheng Chang ◽  
Yan Wang ◽  
Hexiang Zhao ◽  
Shuteng Wang ◽  
...  
Keyword(s):  
Carboxymethyl Cellulose ◽  
Recovery Efficiency ◽  
High Recovery ◽  
Ion Imprinted ◽  
Facile Fabrication ◽  
Ion Imprinting ◽  
Removal And Recovery

An Fe3O4/carboxymethyl cellulose magnetic biosorbent was prepared by ion-imprinting technology, showing good adsorption and selectivity properties for La(iii) with a high recovery efficiency.

scholarly journals Dynamic Repair and Robust Optimization of Complex Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Pengtao Zhang ◽  
Peng Bai ◽  
Chaoqi Fu ◽  
Shanshan Li
Keyword(s):  
Energy Transfer ◽  
Repair Process ◽  
Recovery Efficiency ◽  
Repair Strategy ◽  
Coupling Structure ◽  
Secondary Failure ◽  
Fundamental Reason ◽  
Network Functions ◽  
The Impact ◽  
Failure Node

Network repair is indispensable for maintaining network security. Conventional static repair is relatively inefficient. In this study, by considering the energy transfer between nodes, a dynamic repair model was established. The fundamental reason for the secondary failure of repaired nodes during the dynamic repair process is the coupling structure of failure networks. A dynamic repair strategy was proposed that can effectively prevent the secondary failure of repair nodes influenced by energy during repair and can cause the redundant capacity of repair nodes to be used reasonably. By turning off the energy transfer function of the link to control the excessive flow of energy into the repair node to avoid the occurrence of secondary failure; on the other hand, by sharing part of the load of the failure node, realize the rational use of the redundant capacity of the repair node to reduce the impact of the failure node on the overall function of the network. The proposed strategy mitigated the effect of failure nodes on network functions and substantially improved the recovery efficiency of network functions. Furthermore, redundant edges, behaving as energy redundant links in a network structure, can considerably improve the robustness of the network by optimizing the removal of redundant edges. Dynamic repair is not only an efficient repair method but also a highly flexible choice for network repair.

scholarly journals Prediction of Trace Metal Distribution in a Tailings Impoundment Using an Integrated Geophysical and Geochemical Approach (Raibl Mine, Pb-Zn Alpine District, Northern Italy)

2021 ◽  
Vol 18 (3) ◽  
pp. 1157
Author(s):  
Nicolò Barago ◽  
Stefano Covelli ◽  
Mara Mauri ◽  
Sara Oberti di Valnera ◽  
Emanuele Forte
Keyword(s):  
Electrical Resistivity ◽  
Metal Distribution ◽  
Tailings Impoundment ◽  
Secondary Recovery ◽  
Chemical Content ◽  
Bulk Chemical ◽  
High Concentrations ◽  
Ground Penetrating ◽  
Tailings Ponds ◽  
Geochemical Approach

When mines are decommissioned, tailings piles can act as sources of contamination for decades or even centuries. Tailings, which usually contain high concentrations of metals and trace elements, can be reprocessed for a secondary recovery of valuable elements with an innovative approach to a circular economy. This study offers new results for tailings ponds characterisation and chemical content prediction based on an integrated geophysical-geochemical approach. The study of the Raibl Pb-Zn tailings impoundment was done using bulk chemical analysis on borehole samples, Electrical Resistivity Tomography surveys, and Ground Penetrating Radar measurements. We found valuable and statistically significant correlations between the electrical resistivity of the mining impoundments and the metal distribution, thus providing a practical opportunity to characterise large volumes of metal-bearing tailings. In particular, these results can be useful to aid in the development of environmental monitoring programs for remediation purposes or to implement economic secondary recovery plans.

Waterflooding in Carbonate Reservoirs: Does the Salinity Matter?

2014 ◽  
Vol 17 (03) ◽  
pp. 304-313 ◽  
Author(s):  
A.M.. M. Shehata ◽  
M.B.. B. Alotaibi ◽  
H.A.. A. Nasr-El-Din
Keyword(s):  
Oil Recovery ◽  
Sudden Change ◽  
Oil Production ◽  
Deionized Water ◽  
Production Performance ◽  
Shallow Aquifer ◽  
Secondary Recovery ◽  
Tertiary Recovery ◽  
The Impact ◽  
Indiana Limestone

Summary Waterflooding has been used for decades as a secondary oil-recovery mode to support oil-reservoir pressure and to drive oil into producing wells. Recently, the tuning of the salinity of the injected water in sandstone reservoirs was used to enhance oil recovery at different injection modes. Several possible low-salinity-waterflooding mechanisms in sandstone formations were studied. Also, modified seawater was tested in chalk reservoirs as a tertiary recovery mode and consequently reduced the residual oil saturation (ROS). In carbonate formations, the effect of the ionic strength of the injected brine on oil recovery has remained questionable. In this paper, coreflood studies were conducted on Indiana limestone rock samples at 195°F. The main objective of this study was to investigate the impact of the salinity of the injected brine on the oil recovery during secondary and tertiary recovery modes. Various brines were tested including deionized water, shallow-aquifer water, seawater, and as diluted seawater. Also, ions (Na+, Ca2+, Mg2+, and SO42−) were particularly excluded from seawater to determine their individual impact on fluid/rock interactions and hence on oil recovery. Oil recovery, pressure drop across the core, and core-effluent samples were analyzed for each coreflood experiment. The oil recovery using seawater, as in the secondary recovery mode, was, on the average, 50% of original oil in place (OOIP). A sudden change in the salinity of the injected brine from seawater in the secondary recovery mode to deionized water in the tertiary mode or vice versa had a significant effect on the oil-production performance. A solution of 20% diluted seawater did not reduce the ROS in the tertiary recovery mode after the injection of seawater as a secondary recovery mode for the Indiana limestone reservoir. On the other hand, 50% diluted seawater showed a slight change in the oil production after the injection of seawater and deionized water slugs. The Ca2+, Mg2+, and SO42− ions play a key role in oil mobilization in limestone rocks. Changing the ion composition of the injected brine between the different slugs of secondary and tertiary recovery modes showed a measurable increase in the oil production.

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