Porosity and Permeability Estimation through Seismic Data Integration

A Research Proposal

Ph.D PETROLEUM ENGINEERING RESEARCH PROPOSAL

Title

Petrophysical Properties Estimation through Dynamic Data

Integration

Proposed Supervisors

Prof. Stow

Project Type Academic Research (Ph.D)

Research Project

Description and Problem

Definition

The research work aims to deal with the problem of estimating the

distributions of permeability and porosity in a petroleum reservoir

by matching the dynamic behavior. Permeability and porosity are

the parameters that have the largest influence in determining the

performance of the reservoir, and yet they are the most difficult

properties to determine in reservoir characterization. This work

addresses the problem of estimating the parameters from a variety

of measurements that are only indirectly related to them.

Estimating permeability and porosity is difficult for the following

reasons:

Permeability and porosity have spatial variability

There are very few sampling locations (wells) compared to the

areal extent of the reservoir

Information (data) is scarce

Measurements are obtained with different technologies.

The mathematical model of the reservoir is very complex,

usually consisting of a numerical reservoir simulation.

This work plans to integrate dynamic data in the form of field

measurements from well testing, production history, interpreted 4D

seismic information, and other data such as correlations between

permeability and porosity, geostatistics in the form of a variogram

model and the inference of large scale geological structure so as to

have a good estimate of the parameters.

Devising the optimal strategy for the development of an oil or gas

reservoir is an important and difficult task. Many mathematical

techniques for optimization can be used to deal with problems in

engineering and economics systems. These techniques assume that

we have a fairly complete understanding of the problem and also

that we can construct a mathematical model that predicts the

system's performance accurately in time under different scenarios;

this is not a serious concern in most engineering problems since the

parameters that define the system may not be very difficult to

obtain by direct measurement. Unfortunately this is not the case in

reservoir engineering, where the system, that is the oil and gas

reservoir, is physically inaccessible many thousands of feet

underground. Thus, any serious attempt at optimization of reservoir

development first requires the determination of the parameters of

the reservoir and the only way to obtain them is through indirect

measurement.

Since data is being collected almost continuously, the process of

updating the reservoir model never ends. During the producing life

of a reservoir, data of different nature are always being collected.

These data can be classified as static or dynamic depending on their

association with the movement or flow of fluids in the reservoir.

Data that have originated from geology, electrical logs, core

analysis, fluid properties, seismic and geostatistics can be generally

classified as static, whereas the information originating from well

testing, pressure shut{in surveys, production history, bottom hole

pressure from permanent gauges, water-cut, and gas oil ratio can be

classified as dynamic. With 4-D seismic information, it is possible to

estimate the areal distribution of change of saturations in the

reservoir due to the production or injection of fluids. One of the

outstanding features of the 4D seismic information is that it is

areally distributed whereas the other dynamic data are available

only at the location of the production or injection wells. The process

of handling different data simultaneously is known as data

integration. So far, most of the success in data integration has been

obtained with static information. The parameter estimation

problem would not only be faster but also more reliable if it were

performed with a process that uses all or at least most of the

information in the reservoir data set simultaneously. Remarkably, it

has not yet become common to completely or systematically

integrate dynamic data with static data and it is currently the

subject of major research effort in several places. This work will

address this specific problem and will represent a number of steps

in the direction of full integration.

The behavior of the reservoir will be modeled with a finite

difference numerical simulator because of the requirement of a

mathematical model that is sufficiently complex to accommodate all

the types of the dynamic data that will be used. This will also allow

the application of the approach to heterogeneous reservoirs,

multiphase flow and multiple well problems. The key problem

envisaged in this approach is in the efficient computation of the

derivatives of the field observations with respect to parameters that

define the distributions of permeability and porosity in the

reservoir.

Research Goal

To develop a robust reservoir parameters estimation technique with

a mathematical modeling process that integrates dynamic data with

static data using a systematic approach involving synchronized

techniques of well test interpretation, history matching, reservoir

simulation, geological modeling

Technical Objectives

To investigate the role of time-lapse seismic data for

estimation of key reservoir parameters within the data

assimilation framework

To combine dynamic information (which includes production

history, well test data data from permanent bottom hole

pressure gauges, and changes in the saturation distribution:

from 4D seismic interpretation) with the large scale geological

information to produce key reservoir parameters estimates

(porosity and permeability) that have a good history match to

all available data and predictive capabilities, and at the same

time contain the features present in a true field.

To include core analysis data and geostatistical information

about the spatial correlation of permeability using a

variogram model.

Benefits Porosity and permeability are the most important properties of

reservoir rock that have big influence on the ability of the fluids to

flow through the reservoir and often determine the strategies used

during oil recovery. Usually they have the largest impact on reserves

and production forecasts, and consequently on the economy of a

project. An accurate estimation of the spatial distribution of porosity

and permeability translates into higher success rates in infill drilling,

and fewer wells required for draining the reservoir.

Knowing the spatial distribution of rock properties, one would be

able to design the production strategy to postpone water

breakthrough in the wells and maximize the recovery.

If the numerical model would adequately describe the real

reservoir, it would be possible to predict the reservoir behaviour

properly and plan optimal strategies to maximize the recovery from

a given field.

Background Of the several properties of the porous rock which are important for

oil extraction, porosity and permeability are the most difficult to

estimate. The difficulty of estimating them comes from the fact that

porosity and permeability may vary significantly over the reservoir

volume, but can only be sampled at well locations, often using

different technologies at different scales of observation. All other

reservoir properties can be quantified more easily. For example, we

can measure fluid properties over the expected range of reservoir

thermodynamic conditions through relatively simple laboratory

tests. Likewise, the external geometry of a reservoir can generally

be determined using surface seismic and well-established

interpretation techniques. Porosity data, on the other hand, can

only be measured in core samples, or inferred from density, sonic,

and/or neutron logs along well paths. Permeability is even harder to

predict: lab measurements provide information about its absolute

value at the core scale, but the only way to obtain permeability

estimates at a larger scale is through transient pressure tests, which

may yield an average of permeability over the drainage area of a

well. The internal distribution of lithology and facies in a reservoir,

and the inherent variation in porosity and permeability, remains

beyond the resolution of most geophysical methods. Such lithology

variations can be determined only in cases where conditions favor

the application of advanced seismic interpretation techniques,

which must be supported by a sound rock physics analysis of the

reservoir being evaluated. Even in those cases the predictions have

a limited degree of certainty, which has been the driving force

behind the recent academic and industrial interest on probabilistic

approaches to estimate petrophysical properties from seismic data.

Porosity and permeability are the most important properties of

reservoir rock that have big influence on the ability of the fluids to

flow through the reservoir and often determine the strategies used

during oil recovery. Knowing the spatial distribution of these rock

properties is of particular importance in secondary recovery. It

happens very often that there exist preferential paths through

which injected fluid is moving toward the production well. All the oil

that is located outside this path is not influenced by the injection of

fluids. This causes the production of injected fluid instead of oil at an

early stage. Due to heterogeneous character of the reservoir rock,

the water or gas injected during the secondary recovery phase flows

with different velocities in different parts of the reservoir. If there

exists a preferential path through which injected fluid is moving

toward the production well, the oil located outside this path

remains unflooded and often the production of injected fluid

instead of oil occurs soon after the start of injection (“early

breakthrough”). To avoid early water breakthrough in the wells,

one can try to optimize the production scenarios, by controlling the

injection and production in the existing wells. In the long-term these

proactive strategies should yield higher recovery factors than in the

case of reactive control only (no action is undertaken until

significant changes are observed in the wells). Knowing the spatial

distribution of rock properties, one would be able to design the

production strategy to postpone water breakthrough in the wells

and maximize the recovery. However, the spatial heterogeneity and

lack of direct measurements of rock properties, which are only

known in well locations, introduces a lot of uncertainty that needs

to be addressed if reliable future predictions of reservoir

performance are to be expected.

After the exploration phase, in which potential reservoirs are

identified and exploration wells are drilled, initial geological models

are created based on the knowledge obtained from seismic surveys

and well data. Initial predictions for the future reservoir

performance are made, and if those predictions are economically

profitable the reservoir enters a field development phase. When

developing a field, the main target is to maximize the economic

criterion, most often in terms of oil and gas revenues. Choices are

made about the number and locations of wells, the surface facilities

that need to be built and the required infrastructure. Based on all

available information a detailed geological model of a given

reservoir is created, of which an upscaled simpler version is used for

flow simulation. This numerical reservoir model should ideally mimic

all the processes occurring in the reservoir itself. If the numerical

model would adequately describe the real reservoir, it would be

possible to predict the reservoir behaviour properly and plan

optimal strategies to maximize the recovery from a given field.

Unfortunately, a numerical reservoir model is only a crude

approximation to the truth, mainly for two reasons. Firstly, not all

the processes occurring in a real reservoir can be modelled in an

appropriate way. Very often some simplifications are imposed on

the model, to make the problem easier to tackle. Secondly, there is

usually a large uncertainty in the parameter values of the simulation

model. Many rock properties that influence reservoir flow are

poorly known, while there are also uncertainties in fluid properties

and the amount of hydrocarbons present in the reservoir. The

uncertainties involve the reservoir structure, the initial fluid

contacts, and the permeability values, porosities, and fault

transmissibilities, etc. These reservoir related parameters are

assumed to be known in numerical simulations. However, neglecting

the uncertainties leads to results produced by numerical reservoir

models that contradict the data gathered from the real field. It’s

then difficult to make decisions based only on the output of a

numerical model. Therefore, the measured data together with

numerical simulations should be used in reservoir management for

improving the production rates and increasing the recovery from a

field.

Usually production history data, obtained from wells in the form of

wellhead or bottom hole pressures and flow rates, is used in history

matching algorithms, to update the uncertain parameters. This type

of data is typically acquired with an accuracy between 5%-20%.

However, because the number of model parameters to be

estimated is very large, production history data has a limited

resolving power. It does provide some information on the unknown

properties in the neighbourhood of the wells, but not further away

from them. As a result, there are many reservoir models that give

rise to the same production history data, but yield different

predictions for the future performance of the reservoir.

On the other hand, time-lapse seismic data could also be used

update the uncertain parameters. Due to developments in

geophysics, especially in the field of seismic, it becomes possible to

determine not only the position of the reservoir, but also to track

the fluids movements in the reservoir itself. This additional

information in the form of time-lapse seismic data can be utilized,

together with production data, to narrow the solution space when

minimizing the misfit between gathered measurements and their

forecasts from the numerical model. Time-lapse seismic is the

process of carrying out a seismic survey before the production

begins and then repeating surveys over the producing reservoir.

Seismic data is sensitive to static properties like e.g. lithology, pore

volume, net/gross ratio but also to dynamic (i.e. time varying)

properties like fluid saturation and pore pressure. From one single

seismic survey one is not able to differentiate between features

caused by static properties and those caused by dynamic properties.

By comparing two different seismic surveys acquired over the

producing reservoir at different times, however, it is possible to

extract information about the changes in dynamic properties. It is

possible to include an interpretation step in which the direct seismic

measurements are inverted to produce variables that can be

represented in a reservoir model or in a rock physics model.

The inverted seismic data is then used together with production

data as input into a data assimilation scheme. Although less ac-

curate than production data, time-lapse seismic contains

information about the reservoir properties everywhere and can be

used to infer parameter values away from wells.

Due to different spatial and time scales, history data and seismic

data sets were, and often still are, used separately in updating

reservoir models, resulting in the updated reservoir models which

differ significantly from each other. The models updated in this way,

would often contradict some of the observations obtained from the

true reservoir. With combined use of production and seismic data

one can constrain the inversion in such a way that the final

estimates resemble to some degree the true model.

Project Scope

Review the literature to identify some important areas that

were poorly developed and target them for this work

Review the challenges in the estimation of reservoir

parameters and historical efforts in this regard

Review the general mathematical formulation employed in

estimating reservoir parameters by history matching

Adopt a numerical reservoir simulator as the mathematical

model which will allow the inclusion of oil and gas flow as well

as the modeling of complex reservoir, and also simulator that

will allow the integration of 4D seismic data

Develop an algorithm for reservoir parameters estimation

especially porosity and permeability distribution; an algorithm

that would as well be efficient in computation of coefficient of

parameter variations in a reservoir for proper estimation of

parameters.

Adopt a procedure that will preserve object-based reservoir

models and ensure its consistency when changing reservoir

parameters as a way to preserve the geological information in

the model, and adopt an optimal prediction method to

maintain consistency in geostatistical information in the

resulting interpretation through spatial correlation of the

parameters.

Analyse the variance of the parameter estimates and

resolution for homogeneous and heterogeneous reservoirs by

using an appropriate algorithm

Demonstrate the practicality of the intended approach of this

research work through real field application.

Existing Data Results generated with the model will be validated with yet to be

identified real field data. In this work the field observations, or data

for the parameter estimation problem are:

Extended bottom hole pressure history

Production rates, water cut, and gas oil ratio

Distributed saturation data: Change of saturation distribution

in the reservoir in a given time interval (4D seismic data).

Key References

Relevant Petroleum Engineering Books, Literatures on Reservoir

modeling and simulations, Previous works on reservoir parameters

estimation including, but not limited, to the following:

i. Carter, et al: Performance Matching with Constraints," Soc.

Pet. Eng. Journal (April 1974) 187{196.

ii. Dadashpour M et al: Porosity and permeability estimation by

gradient-based history matching using time-lapse seismic data

15th SPE Middle East Oil & Gas Show and Conf. (Bahrain, 11–

14 March) SPE 104519. 2007

iii. Dadashpour M et al: Non-linear inversion for estimating

reservoir parameters from time-lapse seismic data

Quantitative Methods for Reservoir Characterization Conf.

(IFP, Rueil-Malmaison).2006

iv. Echeverria Ciaurri D et al: Optimal updating of reservoir facies

models by integrating seismic and production data Proc. VIII

Int. Geostatistics Congress (Santiago de Chile, Chile, 1–5

December). 2008

v. Ewing, R. E.: The mathematics of reservoir simulation:

Frontiers in Applied Mathematics. 1983

vi. Guohua Gao et al. A Stochastic Optimization Algorithm for

Automatic History Matching. SPE Annual Technical

Conference and Exhibition, 26-29 September 2004, Houston,

Texas

vii. Jenkins C. D et al: Reservoir Characterization Constrained to

Well Test Data: A Field Example

viii. Leitao H C and Schiozer D J 1999 A new automated

historymatching algorithm improved by parallel computing,

SPE53977 SPE Latin American and Caribbean Petroleum

Engineering Conf. (Caracas, Venezuela, 21–23 April)

ix. Maschio C. et al: A framework to integrate history matching

and geostatistical modeling using genetic algorithm and direct

search methods J. Pet. Sci. Eng. 63 34–42

x. Mohsen Dadashpour et al : A derivative-free approach for the

estimation of porosity and permeability using time-lapse

seismic and production data. 2010 J. Geophys. Eng. 7 351

xi. Porosity and permeability estimation by integration of

production and time-lapse near and far offset seismic data.

Mohsen Dadashpour et al 2009 J. Geophys. Eng. 6 325

xii. Shah P C, Gavalas G R and Seinfeld J H 1978 Error analysis

inhistory matching: the optimum level of parameterization

Soc. Pet. Eng. J. 18 219–28

xiii. Tao Feng and Trond Mannseth: Improvements on a

predictor–corrector strategy for parameter estimation with

several data types . 2009 Inverse Problems 25 105012

Work Schedule

Gantt Chart:

Cost Budget/ Cost Estimate:

Local Transport:

Field Data Collection:

Modeling/Data Analysis:

Report Preparation:

Miscellaneous:

Key Deliverables

Dissertation/ Monograph, SPE Paper; Power Point Presentation,

Virtual Basic (Software) Programming