how to improve the practices in defining oil and gas fields development

How to improve the current

poor Practices in Defining

Fields Development

Giuseppe [email protected]

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.

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 your 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?


How we can efficiently used our data for

understanding and planning, avoiding to

jeopardize them intrinsic value with risky

data manipulation?

The only why to do this is to apply the

consolidated Petroleum Engineering

Methodologies avoiding shortcuts.


Reservoir Engineering, if not

supported by standard consolidated

Petroleum Engineering Methodologies,

could become a gambling and the

Fiend 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

Bubblepoint

ExpandingGas Cap

Liquid shrinkingdue to liberationof dissolved gas

Oil+

dissolvedgas

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”. Paper is to be presented at the Petroleum Society’s 6th Canadian International Petroleum Conference (56th Annual Technical Meeting), Calgary, Alberta, Canada, 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 pure Science.”

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 response, Fluids Pressure Gradient, 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.

On the above there is nothing shocking. They are just best practices

enough to avoid big mistakes if adopted.

Thank you for your attentionGiuseppe Moricca

Jan 2018

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