Analysis of Production Data to Improve Characterization of In-situ 
Megascopic Reservoir Permeability

Chugh, S.; Herweijer, Joost; Kuppe, F.

doi:10.2523/59761-ms

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Power exponents of -1, 0, and 1 represent harmonic, geometric and arithmetic averages of permeability, respectively. 6 A harmonic average permeability is associated with flow that is orthogonal to "stacked" intervals and represents the lowest possible effective permeability as it is limited by the lowest permeability interval. When flow is parallel to these intervals (arithmetic average), a maximum effective permeability is realized.…”

Section: Figure 5: Map Of Idc Derived √Khx F the Physics Behind The Cmentioning

confidence: 99%

Reservoir Quality Mapping Based on IDC Analyses and Hydraulic Frac Size

Kuppe¹,

Carlson²

2011

All Days

Self Cite

View full text Add to dashboard Cite

A three part process has been developed to generate a correlation which can be used to 1) map representative Øh in the Kakwa Cadotte reservoir and 2) optimize the design of hydraulic fracture treatments. This same process can be applied to multi-fractured horizontal wells, completed in the Upper Montney reservoir, to generate a relationship between Estimated Ultimate Recovery (EUR), Øh and total hydraulic fracture tonnage.The initial stage of the process required the application of IDC (Inverted Decline Curve) technology to determine independent quantities of 1) reservoir quality (kh) and 2) hydraulic fracture placement (as measured by fracture half length = X f ). Using this IDC technique to differentiate between the better and lesser quality wells we can find a correlation between Estimated Ultimate Recovery (EUR) and some function of porosity, net pay and fracture tonnage (the second step in this process).The physics behind this relationship essentially correlates to hydraulic fracture volumetrics and effective insitu permeability within each well's drainage area. Wells that do not match the trend established by the "clear majority" of wells (data points) can now be readily flagged and included in the established correlation by modifying Øh and/or frac tonnage until they match the trend. This adjustment would of course have to be rationalized by either a change in 1) possible kh, throughout the well drainage area, or 2) the effective frac tonnage displaced for the well. Given that most of the two dozen wells evaluated established an excellent correlation, this technique became very useful in narrowing the range of possible kh or effective X f , within each well's drainage area. After establishing the relative contribution to EUR of 1) reservoir quality and 2) hydraulic fracture effectiveness, we could map a representative kh distribution to optimize future well locations.

show abstract

Section: Figure 5: Map Of Idc Derived √Khx F the Physics Behind The Cmentioning

confidence: 99%

Reservoir Quality Mapping Based on IDC Analyses and Hydraulic Frac Size

Kuppe¹,

Carlson²

2011

All Days

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yet its importance on the productivity and therefore on the economics of gas fields is paramount (8) . The elusive nature of permeability is, in my opinion, mainly due to its scale dependency.…”

Section: Permeability Diagnosticsmentioning

confidence: 99%

“…Unfortunately, baffling and fracturing often cause scale effects that cause dramatic differences between small scale permeability and large scale permeability (8) . Note that, as implied, as permeabilities decrease, the correction factors increase.…”

Section: Permeability Diagnosticsmentioning

confidence: 99%

The Importance of Comprehensive Gas Field Surveillance

Baker

2005

Journal of Canadian Petroleum Technology

View full text Add to dashboard Cite

Outline of Problem Gas field surveillance historically has not been given the same priority as oil field surveillance because of a couple of significant stumbling blocks: first, the apparent simplicity of gas reservoirs- at least upon initial examination; and, second, the historically low commodity prices of gas relative to oil. The presumption that gasfield surveillance was easily derived from the knowledge that, in most gas pools, single phase flow existed, leading to the assumption that "tank like" behaviour was occurring. However, on examination of hundreds of gas pools, my viewpoint on that matter has changed dramatically. Of course, today's commodity prices are also changing both the relative and absolute perceptions about the economic importance of gas. Initially, in the Western Canadian Sedimentary Basin (WCSB), industry encountered and dealt with very high quality gas resources, such as those found in Leduc reservoirs. These reservoirs had high permeability and were not compartmentalized; this naturally led to the "tank model" approach. These pools had behaviour as illustrated by Figure 1(a). In the last few years, we have moved from what I would term "Tier One" resources to lower quality ones, as shown in Figure 2. Indeed, a large portion of reservoirs do not exhibit that "tank like" behaviour shown in Figure 1(a); instead, there is often a fairly large pressure gradient across pools and non-tank like behaviour, such as that shown in Figure 1(b). This paper stresses the importance of comprehensive surveillance for those types of fields, using simple methods of production analysis and pressure diagnostic tools. There has been an explosion of research on using gas rate decline methods to characterize reservoirs with an effective permeability less than 0.1 mD (k < 0.1mD). However, the transient gas rate decline and practical analysis of such low permeability gas reservoirs are not the focus of this paper. This paper instead focuses on gas field surveillance for multi-well pools in which wells are in boundary dominated flow. The objectives of any gas surveillance program are relatively simple:What is the Original Gas in Place (OGIP)?;What is the current gas in place?;Where is the current gas in place located?;What is the permeability or capacity (kh) of the wells and pool?;What are the limiting considerations to gas recovery factor (low permeability, liquid lifting problems, low continuity, high back pressure, or water influx)?;What can be done to improve the rate of recovery and the recovery factor? and,Can this be done economically? In order to understand the above questions, we must relate the static model (geological/petrophysical) to the dynamic (production/ pressure data) one. Surveillance Methodology The following work flow has proven to be useful in determining the true potential of gas reservoirs:Examine the production profile of individual wells and the field. Examine the initial production rates (IP), interference effects, types of decline, and decline rates (production diagnostics);

show abstract

Automatic Fracture Network Model Update Using Smart Well Data and Artificial Neural Networks

Al-Anazi

Babadagli

2008

Europec/Eage Conference and Exhibition

View full text Add to dashboard Cite

This paper presents a new methodology to continuously update and improve fracture network models. We begin with a hypothetical model whose fracture network parameters and geological information are known. After generating the "exact" fracture network with known characteristics, the data were exported to a reservoir simulator and simulations were run over a period of time. Intelligent wells equipped with downhole multiple pressure and flow sensors were placed throughout the reservoir and put on production. These producers were completed in different fracture zones to create a representative pressure and production response. We then considered a number of wells of which static and dynamic data were used to model well fracture density. When new wells were drilled, historical and new data were used to update the fracture density using Artificial Neural Networks (ANN). More dynamic data will be provided as well as more static data when additional wells are drilled. The accuracy of the prediction model depends significantly on the representation of the available data of the existing fracture network. The importance of conventional data and smart data prediction capability was also investigated. A highly sensitive input data was selected through forward selection scheme to train the ANN. Well geometric locations were included as a new link in the ANN regression process. Once the relationship between fracture network parameters and well performance data was established, the ANN model was used to predict fracture density at newly drilled locations. Finally, an error analysis through correlation coefficient and percentage absolute relative error performance was performed to examine the accuracy of the proposed inverse modeling methodology. It was shown that fracture dominated production performance data collected from both conventional and smart wells allow automatically updating the fracture network model. The technique proposed helps in generating another -readily available at no cost- data source for fracture characterization to be used as supplementary to limited 1-D data obtained from well logs and cores. Introduction Naturally fractured reservoirs (NFR) are characterized by their fracture network properties, such as fracture density, orientation, location, dimension, and connectivity. The network properties control the fluid flow in the reservoir and therefore, accurate prediction of those parameters is essential in generating the static model (fracture network) to be used for performance prediction. Several different approaches have been utilized based on statistical, fractal, and Artificial Neural Network (ANN) methods to build a conditioned stochastic fracture network models. Fracture statistics and distribution functions are traditionally extracted from core, well log, outcrop, and seismic data. Intelligent fields in which reservoir surveillance data are continually measured using permanently installed downhole completion devices have been increasingly becoming popular. Multiphase production data are transmitted to engineers to monitor field operation and make effective decisions. The readily available conventional and smart well production information, referred to as dynamic data, could be also useful in generating static models of NFRs. In our previous attempt, we showed that there exists a non-linear relationship between dynamic data and fracture network characteristics (Al-Anazi and Babadagli, 2007). In that study, ANN was used to detect the underlying relationship. Our inverse problem consists of predicting fracture network characteristics using limited static and readily available historical well performance (dynamic) data. The solution of such an inverse problem is ill-posed in general and cannot uniquely constrain the detailed variations in reservoir static properties. The objective of this study was to investigate the importance of smart (permanent downhole devices) and conventional (well surface devices) dynamic data in generating the fracture network maps.

show abstract

Analysis of Production Data to Improve Characterization of In-situ Megascopic Reservoir Permeability

Cited by 3 publications

References 0 publications

Reservoir Quality Mapping Based on IDC Analyses and Hydraulic Frac Size

Reservoir Quality Mapping Based on IDC Analyses and Hydraulic Frac Size

The Importance of Comprehensive Gas Field Surveillance

Automatic Fracture Network Model Update Using Smart Well Data and Artificial Neural Networks

Contact Info

Product

Resources

About