How to improve the practices in defining fields development

How to improve the current
poor Practices in Defining
Fields Development
Giuseppe Moricca
moricca.giuseppe@libero.it

This presentation is structured to provide
the:
 Identification of root causes of failure
in reaching the planned oil production
target.
 Strategy and practical advices to face
the problem of project plan failure.
January 2018 G. Moricca 2
Content

Oil and gas industry's Failure to reach
Production Targets
 On 2011 has been published an SPE paper enlightening the
continuous degradation of the oil and gas industry's capability in
reaching their declared field development production attainment
target.

Quantification of the Failure
 Based on analysis conducted on over 145 oil and gas projects, the
average oil and gas project delivers only 75 barrels for every 100
barrels promised at sanction.
 According to the
authors of this analysis,
the problem persist
because companies do
a poor job of
conducting root-cause
analysis to understand
production shortfalls.
 This happen despite improvements in reservoir evaluation tools
and techniques for hydrocarbon withdrawal.

Reasons of the Failure
 According to the authors of the analysis, the root causes
of the failure fall into four broad categories:
- Lack of basic reservoir data or reliance on incomplete or
assumed reservoir data.
- Failure to learn and plan from past experiences.
- Poor quality of sanction production forecasts.
- No single point of accountability for production performance.

Main causes of the Failure
 Reservoir related problems
have the largest and most
lingering effect on
production.
 Incomplete or poor quality reservoir data: contaminated fluid
samples, poor PVT analysis, incomplete pressure survey, partial
knowledge of the areal distribution of fluids saturation, poor
knowledge of the vertical and horizontal areal transmissibility, etc.
 This means that project
teams are forced to make
assumptions about missing
data or about remaining
risks in their production
forecasts.

Root causes of the failure reservoir related
 Some of root causes identified by the study:
- Reservoir more compartmentalized than expected.
- Major reduction in plateau rate due to lower than
assumed recovery factor.
- Assumed continuous sand sheet model; turns out not
to be the case (Reservoir discontinuity).
- Static model was very optimistic. Model predicted P50
permeability of 5mD while actual was 1mD, less than
P10 (Poor rock quality: low permeability).

How to avoid project failure
 Ingredients to avoid project failure:
- Collection of good and exhaustive reservoir input
data through a consistent Formation Evaluation.
- Understanding of the physics governing the
phenomena achievable by the adoption of the
standard consolidated Petroleum Engineering
Analysis methodologies.
- For forecasting purpose, generate a reservoir
numerical model consistent with the Petroleum
Engineering analysis avoiding arbitrary
assumptions.

Basic of Formation Evaluation
 A proper Formation Evaluation is the essential pre-
requisite for a reliable field development capable to
generate high economical value.
 Formation Evaluation involves detailed and systematic
data acquisition, gathering, analysis and interpretation
both qualitatively and quantitatively.
 A poor Formation Evaluation can be the source of
misunderstanding and can generate the loss of
opportunity associated with significant economical losses.
 Formation Evaluation should be managed as a strategic
investment activity of the Company.

Goal of Formation Evaluation
 The main goals of Formation Evaluation are:
- Evaluate the presence or absence of commercial
quantities of hydrocarbons in formations
penetrated by, or lying near the wellbore.
- Determine static and dynamic characteristics of
reservoir.
- Detect hydrocarbon for commercial exploitation.

What data/information are we interested in
for a reliable full field development?
 Rock Type
 Porosity
 Fluid Type
 Fluid Saturation
 Permeability
 Reservoir Pressure
 Reservoir Structure
 Reservoir Drive Mechanism

Formation Evaluation Methods
 Seismic Survey
 Mud Logging
 Measurement While Drilling (MWD)
 Coring
 Wireline Logging
 Testing and Sampling

Seismic Survey
 Data/information from Seismic Survey:
- Vertical Seismic profile of the earth
- Structure of reservoir
- Location of traps, faults and seals
- Depth of structure and geologic
layer
- Presence of fluids
- Changes in the reservoir over time
by Time Lapse seismic (4D Seismic)
4D Seismic Example

Mud Logging
 A well logging process in which drilling mud and drill bit cuttings
from the formation are evaluated during drilling and their
properties recorded on a strip chart as a visual analytical tool
and stratigraphic cross sectional representation of the well.
 Data/information from Mud Logging:
- Lithology, mineralogy and their estimated depths
- Hydrocarbon shows and type
- Chromatographic analysis of gas
- Hazardous gas e.g. H2S
- Rate of penetration
- Fossil record
- Overpressure zones
- Drill cutting porosity

Conventional Coring
 Taking a core requires that the regular drill bit be removed from
the hole. It is replaced with a "core bit", which is capable of
grinding out and retrieving the heavy cylinder of rock.
 The core bit is usually coated with small, sharp diamonds that
can grind through the hardest rock. A core bit cuts very slowly.
 A core is a solid cylinder of rock about 4-5 inches in diameter,
and a single core will usually be about 30 feet long.

Wireline Log
 Lithologic Logs
- Spontaneous Potential (SP)
- Gamma Ray (GR)
 Porosity Logs
- Neutron
- Density
- Sonic
 Resistivity Logs (Fluid Type)
- Resistivity
- Induction
 Other
- Production Log (PLT)
- Dipmeter
- Caliper
- Temperature
- Acoustic (Cement
Bond Log - CBL)
- Formation Micro
Imager (FMI)
- Many more …

Open Hole Logging Measurement
 Caliper
 Resistivity Logs (Microresistivity,
Laterolog, Induction)
 Radioactive Logs (Gamma Ray, Neutron
Porosity, Density Porosity)
 Sonic / Acoustic Logs (Monopole and
Dipole Sonic)
 Magnetic Resonance
 Dipmeter Logging
 Pressure Testing and Sampling
Dual-Spacing Formation Logging
Device (FDC) – Schlumberger 2010

Cased Hole Logging Measurement
 Radioactive Log
- Gamma Ray
- Neutron Porosity
- Carbon-Oxygen Log
 Sonic - Acoustic
Log
- Cement Bond
Log (CBL)
- Variable
Density Log
(VDL)

Production Logging Measurement
 Nuclear (Gamma Ray)
 Flow meter
 Hold up meter
 Pressure
 Temperature

Formation Testing
Two different technologies can be used for testing:
 Wireline formation testing uses a probe that can be
positioned at a selected depth in the formation to
provide accurate measurements of pressure and fluid
type but limited production data.
 Well testing uses a packer lowered on drillpipe or tubing.
The tested interval is not precisely defined and downhole
measurements are limited, but the volume of fluid
produced enables complete evaluation of production
potential.

Wireline formation testing
 RFT = Repeat Formation Tester
 WFT = Wireline Formation Tester
 MDT = Modular Dynamic Tester
 RCI = Reservoir Characterization Instrument
 FRT = Flow Rate Tester
 These are tests of very
short duration (minutes)
conducted on a wireline,
usually while the well is
drilling.
 The common use is for
determining the reservoir
pressure at various
depths.

RFT Pressure Gradient Interpretation
 This example illustrates the interpretation of RFT data. The incorrect interpretation
generates an unreliable water gradient of 0,577 psi/ft. The correct interpretation
generates an reliable water gradient of 0,45 psi/ft, an oil gradient (0,27 psi/ft)
honoring the PVT measurement and a correct oil column extension.
Incorrect
Interpretation
Correct
Interpretation
From “The Practice of Reservoir Engineering” L. P. Dake - Elsevier 1994
 The correct interpretation reveal that a non-equilibrium situation pertains across
the reservoir and aquifer: there being slight perturbations in pressure of about 5
psi between separate layers.

Formation Testing
Typical MDT configurations for formation testing and sampling

Formation Testing
Pressure Build-up
 The well is shut-in, following one
or more flow periods. The
pressure is measured and
analyzed to give permeability,
skin, average reservoir pressure,
and reservoir description.
 For well evaluation, less than two
days of pressure data.
 For reservoir limit testing, several
months of pressure data.

Well Testing for Reservoir Description
Well test objectives
 Exploration well
- On initial well, confirm HC existence,
Predict a first production forecast (fluid
nature, Pi, reservoir properties).
 Appraisal well
- Refine previous interpretation, PVT sampling, Longer
production test for field delimitation and drive mechanism
identification.
 Development well
- Satisfy need for well treatment (Horizontal Perm, Vertical
Perm, Skin), Interference testing, Average reservoir Pressure
(Pav) for Material Balance and reservoir surveillance purpose.

Well Testing for Reservoir Description
From HERIOT WATT University 2011.

Formation Testing
Interference or pulse test
 These tests involve flowing one well (active)
but measuring the pressure at another well
(observation), and are used to determine
interwell connectivity.
 The signal will be received to the observation
well with a delay and the response is smaller.

Formation Testing
Inflow performance relationship – IPR test
 These tests are designed to yield the long-term deliverability of the well, and are
not concerned with determining the reservoir characteristics The deliverability
test for an oil well is called IPR (inflow performance relationship).
 It describes the inflow into the wellbore at various bottom-hole pressures. The
test consists of a single flow until stabilization is reached, at which time the oil
and water flow rates and the flowing pressure are measured.
 An IPR is plotted according
to known relationships
such as the Vogel IPR
equation.

Asset Protection
 Formation Evaluation requires huge investment in people,
technology and best practices to maximize and protect the value of
the asset.
 Are yours multimillion dollar assets equally protected?
 My city car, that implied an investment of less than 10.000 (ten
thousand) Euro, is protected by a sophisticated real-time data
acquisition system and many diagnostic protocols.

Formation Evaluation Data Storing and
Accessibility
 Were are located your Formation Evaluation data?
 Who take care of it?
 As dedicated PE, what I have to do if I need the most
updated PVT or the most recent average reservoir
pressure?
 Do you have a structured data base specifically dedicated
to the Formation Evaluation data?
 How much time I have to spent to find and retrieve
quality controlled data of interest?

 Our reservoirs are complex systems similar to the outcrops illustrated below.
Value of Core Data
 The only physical evidence
of our reservoirs are the
cores, available on our cores
warehouse.
 How many geologists and reservoir engineers have the habit to go to core
warehouse before starting them work with the numerical simulator to convert
the real in numerical?

The question:
How we can efficiently used our data for
understanding, planning and defining a field
development avoiding to jeopardize them
intrinsic value with risky data manipulation?
A possible (our preferred) answer:
The only way to generate a consistent field
development plan is to apply the consolidated
Petroleum Engineering Methodologies avoiding
arbitrary assumptions and easy shortcuts.

Field Development activities, if not
supported by standard consolidated
Petroleum Engineering Methodologies,
could become a gambling and the
Field Development an economical
catastrophe.

L. P. DAKE, one of the giant on Petroleum
Engineering Practices, gives us “ knife and fork ” to
effectively face large number of Petroleum
Engineering issues.
To avoid
failures, what
we have to do
is just to work
professionally
applying His
teachings.

The Reservoir Engineering Practices
L. P. Dake identified four main areas relevant to the
overall reservoir engineering activity. They are:
- Observations
- Assumptions
- Calculations
- Decisions
Observations
Assumptions
Calculations
Decisions

The Reservoir Engineering Practices
 Observation. It includes the geological model, the drilling of
wells and the data acquired: cores, logs, tests, fluid samples.
 Decisions. Every action contemplated, planned and executed by
reservoir engineers must lead to some form of field development
decision, otherwise it should not be undertaken in the first place.
 Assumptions. Having thoroughly examined and collated all the
available data, the engineer is usually obliged to make a set of
assumptions concerning the physical state of the "system" for
which an appropriate mathematical description must be sought.
 Calculations. Once a physical condition has been defined
(assumed) then calculations are an absolute must.

Some Criticalities in Reservoir Modeling*
 Once the data have been collected and verified, the engineer
must interpret them very carefully and collate them from well-
to-well throughout the reservoir and adjoining aquifer.
 This is a most delicate phase of the whole business of
understanding reservoirs, in which it can prove dangerous to
rely too much on automated techniques.
 Most of the potential criticalities can be overcome by maintain
a strictly adherence between the physics of the phenomenon
and its mathematical description.
* From “The Practice of Reservoir Engineering” L. P. Dake - Elsevier 1994

Some examples of “ statistical smearing ” (*)
 Some examples of the dangers of "statistical smearing", which adversely affect
the calculation of sweep efficiency in waterdrive or gas-drive projects
respectively are:
- The evaluation of formation heterogeneity using probability distributions of
permeability. This totally neglects gravity and therefore disregards Newton's
second law of motion.
- Application of convoluted petrophysical transforms to generate permeability
distributions across formations. Considering the expensive errors this leads
to, it is much cheaper to core "everything".
- The history match process should be not treated as a statistics problem
because there is nothing "random" on it. Statistic approach merely converts
a “pure physics problem” into a “mathematics problem” hiding the
information that reservoir provided us by its behavior and we lose the
opportunity to learn from the “reservoir teaching”. This can happen just
because today standard laptops are capable to perform massive quantity of
calculations.

Very impacting assumptions*
Having thoroughly examined and collated all the available data, the
engineer is usually obliged to make a set of assumptions concerning the
physical state of the "system" for which an appropriate mathematical
description must be sought. For instance:
- The oil or gas reservoir is or is not affected by natural water influx
from an adjoining aquifer.
- There will or will not be complete pressure equilibrium across the
reservoir section under depletion or waterdrive conditions.
- The late-time upward curvature of points in a pressure buildup
survey results from: the presence of faults, dual porosity
behaviour or the breakout of free gas around the wellbore.
Look for physical evidences to take the right decision and
avoid big mistakes.

Material Balance as a fundamental tool in
Field Development Project
January 2018 40
 Material balance analysis is used to determine original
fluids-in-place (OFIP) based on production and static
pressure data.
 The material balance equations considered assume “tank
type” behaviour and do not require any geological
model.
 Material balance analysis is the only technique available
that allows to correlate the reservoir behavior with
measurable reservoir physical parameters: pressures and
volumes.
G. Moricca

Material Balance: The Basic Principle
January 2018 41
p1 > p2 p3 p4> >
Under-
saturated
oil
Bubble
point
Expanding
Gas Cap
Liquid shrinking
due to liberation
of dissolved gas
Oil
+
dissolved
gas
Initial gas cap Expanded gas cap
Expanded of oil +
dissolved gas
Reduction in PV due to
increased grain packing
and connate water
expansionPinit P>
G. Moricca

Material Balance: The Basic Principle
January 2018 42
Fluid Withdrawal (rb) =
Expansion of oil + originally dissolved gas (B) (rb)
+ Expansion of gas cap gas (A) (rb)
+ Reduction in PV due to expansion of connate water
and tighter grain packing (C) (rb)
+ Cumulative water influx (D) (rb)
Pinit P>
A
B
Oil +
dissolved
gas
Gas cap
C
D
G. Moricca

 Material balance is the safest technique in the business since it is the minimum
assumption route through the subject of reservoir engineering.
 Material balance can be applied using simply the production and pressure
histories together with the fluid PVT properties.
 No geometrical considerations (geological models) are involved, hence the
material balance can be used to calculate the hydrocarbons in place and define
the drive mechanism.
 Material balance approach is very useful tool in performing many tasks,
including:
- Confirming the producing mechanism
- Estimating the OOIP and OGIP
- Estimating gas cap sizes
- Estimating water influx volumes
- Identifying water influx model parameters
- Estimate the reservoir pressure for a given production and /or injection
schedule
Material Balance: Why do it?

Necessary conditions for application of Material
Balance From “The Practice of Reservoir Engineering” L. P. Dake - Elsevier 1994
 Adequate data collection, production/pressure/PVT, both in
quantity and quality, otherwise the attempted application of the
technique can become quite meaningless.
 It must be possible to define an average pressure decline trend for
the system under study. The pressures, p, are the average values
within the drainage area of each well.

Necessary conditions for application of Material
Balance From “The Practice of Reservoir Engineering” L. P. Dake - Elsevier 1994
 It is commonly believed that rapid pressure equilibration is a prerequisite
for successful application of material balance but this is not the case; the
necessary condition is that an average pressure decline trend can be
defined, which is possible even if there are large pressure differentials
across the accumulation under normal producing conditions. All that is
necessary is to devise some means of averaging individual well pressure
declines to determine a uniform trend for the reservoir as a whole.

Material Balance Numerical Model
Hydrocarbon
Pore Volume
[HCPV]
Is an OUTPUT
HCPV is the result of reservoir production performance
and insofar reflects the phisical beaviour of the reservoir.
In this respect the model is zero dimensional with the
average pressure determined at a point (datum plane)
which is representative of the reservoir as a whole.
Is an INPUT
Numerical Reservoir model contains a full geological and petrophysical
description of the reservoir necessary to give physical structure to the
model. Accordingly, the HCPV becomes an INPUT.
In this respect, the HCPV reflects the geologists INPUT data without any
direct relation to the reservoir phisical beaviour.
The link among the geological model and the reservoir beaviour is build
by the history match process, that is an interpretation process that
doesn't have any link to any phisics law.
Drive
Mechanism
Is an OUTPUT
The reservoir Drive Mechanism is the result of reservoir
production performance and insofar reflects the phisical
beaviour of the reservoir.
In this respect the model is zero dimensional with the
average pressure determined at a point (datum plane)
which is representative of the reservoir as a whole.
Is an INPUT
Similarly a part of the physical description is the Drive Mechanism
model, one is either (mathematically) attached to the reservoir or it is
not, at the discretion of the engineer. Consequently, the drive
mechanism is also being fed in as an input assumption or partial
assumption.
The main Differences and Peculiarities among
Material Balance and Numerical Approach
If material balance doesn't work it is most likely that the reason is because the data
collection has been inadequate or careless, under which circumstance no technique will
provide sensible answers to reservoir engineering problems.

Some of the most common objections to
Material Balance approach
The most common objections to reject the Material Balance are:
 It requires long of production history to provide reliable
information.
 It requires the wells to be shut-in in order to determine the
average reservoir pressure.
 It doesn't take into account the geology of the reservoir.
 It requires the averaging of reservoir properties, i.e. So, Sg
and Sw.

Material Balance requires long of production history
to provide reliable information
 Yes, in general, a minimum of 10 to 15% of the in-place volume must be produced
before there is sufficient data to identify a trend and reliably extrapolate to the
original in-place volume and extrapolate the drive mechanism parameters. This is the
penalty to be payed for a direct measurement of original volume of hydrocarbons-in-
place . Insofar, the following is suggested:
- Define a Long Production Test [LPT] program at the very early stage of field
appraisal activities to define the original volume of hydrocarbons-in-place and the
water influx model parameters.
- Use the LPT as an “early Production phase” having the double benefice to generate
a “early cash flow” while fundamental information for the full field development
plan will be acquired.
- Continue the LPT during the field development phases for a continuous refinement
of original volume of hydrocarbons-in-place and the drive mechanism
parameters.
- Imbed the dynamic data into the reservoir numerical model in a continuous
dynamic process.

Material Balance requires the wells to be shut-in to
determine the average reservoir pressure (*)
 Well shut-in can be avoided by the adoption of the “Dynamic
Material Balance” technique. “Dynamic Material Balance” is
applicable to either constant flow rate or variable flow rate, and
can be used for both gas and oil.
 The “Dynamic Material Balance” is a procedure that converts
the flowing pressure at any point in time to the average
reservoir pressure that exists in the reservoir at that time. Once
that is done, the classical material balance calculations become
applicable, and a conventional material balance plot can be
generated.
(*) L. Mattar and D. Anderson. Fekete Associates Incorporated. “Dynamic Material Balance”. (56th Annual Technical Meeting, Calgary, June 7 – 9, 2005.
 The Dynamic Material Balance should not be viewed as a
replacement to buildup tests, but as a very inexpensive
supplement to them.

Procedure to convert the flowing pressure to the
average reservoir pressure for variable oil rate (*)
For any flow condition (constant rate or variable rate) the analysis procedure is:
a) Plot a Cartesian graph of (pi - pwf/q) versus Np/q. The early part of the
data may be curved because of transient flow. However, the boundary
dominated flow will yield a straight line with an intercept equal to bpss.
(*) L. Mattar and D. Anderson. Fekete Associates Incorporated. “Dynamic Material Balance” 2005.
b) Convert the measured flowing
pressure to the average reservoir
pressure ( pR ) existing in the
reservoir at that time by the
following equation:
pR = pwf + bpss x q

Material Balance doesn't take into account
the geology of the reservoir.
 This is a vantage not a limitation, because the original in-place volume can be
determined knowing only:
- Oil, gas, water, and rock compressibility.
- Oil formation volume factor (Bo) and solution gas ratio (Rs) at the pressures
considered.
- The amount of free gas in the reservoir at initial reservoir pressure.
- Connate water saturation.
- Production/injection volumes and the associated reservoir pressures.
 In this respect the model is zero dimensional with the average pressure determined
at a point (datum plane) which is representative of the reservoir as a whole. It is this
unique property that permits the attempted solution of the equation to determine
the hydrocarbons in place and define the drive mechanism.
 Material balance can be applied using simply the production and pressure histories
together with the fluid PVT properties. No geometrical considerations (geological
models) are involved.

Material Balance requires the averaging of
reservoir properties, i.e. So, Sg and Sw.
 Yes, the averaging of reservoir properties, (i.e. So, Sg
and Sw) is required and can become a criticality if
only limited data are available.
 Anyhow we cannot escape from this problem that
becomes more complex in reservoir modeling where
the areal distribution of rock and fluids properties is
required. In such case a specific value to each cell of
the model has to be assigned.

Limits of Material Balance approach
 Being Material Balance model independent from the spatial
distribution of the reservoir properties, in this respect the model
is zero dimensional with the average pressure determined at a
point (datum plane) which is representative of the reservoir as a
whole, is an excellent tool to evaluate the reservoir drive
mechanism.
 There should be no competition between material
balance and numerical simulation, instead they must
be supportive of one another: the former defining the
system which is then used as input to the numerical
model.
 But has considerable disadvantages when it comes to prediction,
which is the domain of numerical simulation modelling.

Combined Material Balance and Numerical
Model approach
 To take vantage of both Material Balance and Numerical Model
approach, L. P. Dake proposed the following workflow:

Appraisal Strategy
for Onshore and
Offshore fields

Data collection during the Field Appraisal
 Field appraisal should includes an early production phase to
collect all the data required for the filed delimitation and Drive
Mechanism identification.
 An obvious advantage of early production is that it provides a
positive cash flow from day one of the project.
 Moreover, another greater benefit is that it permits the
reservoirs to viewed under dynamic conditions from the earliest
possible date. Continuous withdrawal of fluids creates a pressure
sink at the location of the discovery well that, with time, will
radiate both areally and vertically throughout the producing
formations.
 The above approach is easily applicable in an onshore project
while in offshore environment is not.

Appraisal of Offshore Field
 Unfortunately, the offshore appraisal wells, which may range in
number from one or two on a small accumulation to twenty or
more on a large, cannot usually be produced on a continuous
basis from the time of their drilling, since the offshore production
and hydrocarbon transportation facilities are not in existence at
this stage of the development.
 Consequently, only data collected under purely static conditions
(no depletion) will be available.
 Therefore, even at the very end of the appraisal stage the reservoir
engineer is confronted with the dilemma of not knowing precisely,
or sometimes even approximately, the degree of pressure
communication both areally and vertically in the reservoirs that
have been appraised.

Emerging
Petroleum Engineering
New Technologies

Petroleum Data Driven Analytics
Shahab D. Mohaghegh (Professor at West Virginia University and President
of Intelligent Solutions, Inc.) provided us with an excellent description of
“Petroleum Data Driven Analytics”:
“Petroleum Data Driven Analytics refers to the collection of tools,
techniques, and methodologies that use data as the starting point,
building blocks, and foundation of analysis, workflows, modeling, and
decision making.”
“The main technologies that are integrated to form Petroleum Data
Driven Analytics include (but are not limited to) traditional statistics,
artificial intelligence including machine learning, and data mining.”
“The objective of Petroleum Data Driven Analytics is to use data in order
to perform one or more of the following: Analysis, Predictive Modeling,
Control (when appropriate) and finally Optimization of the processes in
the oil and gas industry.”

Cont. Petroleum Data Driven Analytics
“Petroleum Data Driven Analytics does not introduce a new discipline in the oil
and gas industry above and beyond the current disciplines such as drilling,
geosciences, reservoir, and production engineering. Petroleum Data Driven
Analytics is an enabler. It is a new tool in the toolbox used by the petroleum
professional. Petroleum Data Driven Analytics provide new approaches in solving
the technical problems petroleum professionals deal with on a daily basis.”
“Petroleum Engineering is a physics-based (and geology-based) area of science
and engineering. As such, it has a long tradition and a track record of dealing with
challenging problems. However, until very recently, all of our solutions have
started by using physics to model the physical phenomena and then mathematics
to reach acceptable solutions to the physics-based models. Petroleum Data Driven
Analytics also attempts to find solutions to most challenging problems that
petroleum engineers face every day. However, instead of our today’s
understanding of physics, in Petroleum Data Driven Analytics we start with data
and not physics. Physics and sometime geology instead of being the starting point
are usually part of the outcome or the solutions generated by Petroleum Data
Driven Analytics. In Petroleum Data Driven Analytics, data always is the starting
point of the analysis, workflows, models, and solutions.”

Very recently, JPT 01 October 2017, Dr Luigi Saputelli, SPE, Senior Reservoir
Engineering Adviser, ADNOC, and Frontender, stated:
“While many other industries have experienced tremendous benefits over the last
few decades, adoption of data-driven analytics is still young in the oil and gas
sectors. Benefits captured across industries involve improving the quality of
decisions, improving planning and forecasting, lowering costs, and driving
operational-efficiency improvements. However, many challenges for full adoption
exist in our industry. In addition to the outdated data-management challenges, key
gaps exist in the understanding of basic principles concerning how and when to use
different data-analytics tools.”
“Data-analytics benefits are being demonstrated through the efficient exploitation
of data sources to derive insights and support making decisions. An exponential
increase in the number of applications in recent years has been observed for
enhancing data quality during/after acquisition by automatically removing noise
and outliers; better assimilating new and high-frequency data into physics-based
models; optimizing calendar-based inspections for preventive-maintenance tasks;
increasing equipment availability of well, surface, and drilling systems; optimizing
reservoir recovery on the basis of injector-to-producer allocation factors; and many
others.”

 Petroleum Engineering is a “Physics-based Interpretation Art not a Science
exact.”
 Science can be defined as “knowledge or a system of knowledge covering
general truths or the operation of general laws especially as obtained and
tested through scientific method.”
My view point:
 Seismic interpretation, Fluids migration, Sedimentary Rock Deposition
Mechanism, Reservoir Geological Setting, Reservoir Pressure Transient
Analysis, Fluids Distribution, Reservoir Fluids Drive Mechanism, and many
others Petroleum Engineering items are both science and art.
 Physics-based Interpretation Art can be defined as “a system or method
reconciling practical experiences with scientific laws.”
 Is Petroleum Data Driven Analytics capable to combine science and
interpretation art?
My question:

Conclusions and Recommendations
 Areas of implementation:
- Better Observations
- Better Assumptions
- Better Calculations
- Better Decisions
 Better Observations: More focused Formation Evaluation to fully understand the
characteristic and peculiarity of the reservoir to be (or not to be) exploited. Do only
what is strictly required to reach the objective, but avoid the shortcuts otherwise you
will pay a “big penalty” later.
 There is large room for Fields Development Practices implementation.
 Better Assumptions: Reduce the assumptions at the minimum required. Be sure that
the assumptions are consistent with the asset characteristics.
 Better Decision: Take your decisions based on calculations and a clear and consistent
strategy.
 Better Calculations: Maximize the quality of your models: PVT model, Reservoir
Model, Well model (sand-face and completion), Surface network model, Piping
model, Fluids Separation model.
Above there is nothing shocking. The bullet points are just the best
practices enough to avoid big mistakes, if adopted.

Thank you for your attention
Giuseppe Moricca
Jan 2018
Target
FDP
Organization
Infrastructures
and Constraints
Know How
Strategy

How to improve the practices in defining fields development

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