MAKING DECISION IN OIL FILED

The research presented in this project is about how to make a decision in phases of the field life cycle. Making a good decision is extremly important, because it will make profit, reduce the risk and uncertainty, it is safe for people and enviromenet. There are many factors that impact to the decison such as the data of discovery, exploration, appraisal, the reserves can recover from the reservoir, the economic of exploration, appraisal, and development; and the affect to enviroment. Decison maker need to analysis each factors and their risks an uncertainties to choose the best choice for the project. In this project, we present four problems. Firstly, the study indentifies what is decision analysis, the tools are used to make decision, and sensitive analysis. Secondly, we present the technique fators that influence making decision – reserves. Thirdly, we analyze economic factors impact to the decision in oil filed, especially the exploration and appraisal phase. And the last, we introduce the EIA report.

FACULTY OF GEOLOGY & PETROLEUM ENGINEERING



Petroleum Project MAKING DECISION IN OIL FIELD LIFE CYCLE

Advisor Assoc Prof Dr Tran Van Xuan

Students Nguyen The Vinh 31204550

Bui Nhat Thinh 31203602

Project Committee : 1.

2.

3.

4.

This project consumed huge amount of work, research and dedication Still,implementation would not have been possible if we did not have a support of manyindividuals Therefore we would like to extend our sincere gratitude to all of them.

First of all we are sincerely grateful to Assoc Prof Dr Tran Van Xuan for provision

of expertise, and technical support in the implementation Without his superiorknowledge ,experience and uncountable enthusiasm, the paper would like in quality

of outcomes, and thus his support has been vital

We also give our thanks to Department for assistance with lots of preciousdocument which moderated this paper and in that line improved the manuscriptsignificantly

Nevertheless, we express our gratitude toward our families and my friends for theirkindness and encouragement which help us in completion of this project

The research presented in this project is about how to make a decision in phases ofthe field life cycle Making a good decision is extremly important, because it willmake profit, reduce the risk and uncertainty, it is safe for people and enviromenet.There are many factors that impact to the decison such as the data of discovery,exploration, appraisal, the reserves can recover from the reservoir, the economic ofexploration, appraisal, and development; and the affect to enviroment Decisonmaker need to analysis each factors and their risks an uncertainties to choose thebest choice for the project

In this project, we present four problems Firstly, the study indentifies what isdecision analysis, the tools are used to make decision, and sensitive analysis.Secondly, we present the technique fators that influence making decision –reserves Thirdly, we analyze economic factors impact to the decision in oil filed,especially the exploration and appraisal phase And the last, we introduce the EIAreport

LIST OF TABLES

CHAPTER 1 DECISION ANALYSIS

1.1. Decision Analysis (DA)

1.1.1 Definition of Decision Analysis (DA)

The historical origins of decision analysis can be partially traced tomathematical studies of probabilities in the 17th and 18th centuries by Pascal,Laplace, and Bernoulli However, the applications of these concepts in business andgeneral management appeared only after the Second World War (Covello andMumpower, 1985; Bernstein, 1996) The problem involving decision-making underconditions of risk and uncertainty has been notorious from the beginnings of the oilindustry Early attempts to define risk were informal

Decision Analysis a methodology based on a probabilistic framework whichfacilitates high-quality, logical discussions, leading to clear and compelling actions

Decision analysis is a scientific and practical method for making important

decisions It was introduced in the 1960s Decision analysis involves identification,clear representation, and formal assessment of important aspects of a decision andthen determination of the best decision by applying the maximum expected valuecriterion Decision analysis is suitable for a wide range of operations managementdecisions where uncertainty is present Among them are capacity planning, productdesign, equipment selection, and location planning

1.1.2 Reasons to use Decision Analysis

DA is a prescriptive approach, based on Decision Science, aimed at helpingpeople to deal effectively and consistently with difficult decisions Carefullyapplying DA techniques will lead to better decisions and better outcomes

DA is an information source, providing insight about the situation, uncertainty,objectives and trade-offs.which should help the decision-maker arrive at acompelling to avoid procrastination and “paralysis by analysis”

1.1.3 Procedure of DA

Determine the goal or objective, e.g., maximize expected profit or net present

Develop a list of possible alternatives for the decision in order to achieve the

goal

Identify possible future conditions or states of nature for each random variable(e.g.,demand will be low, medium, or high; the equipment will or will not fail; thecompetitor will or will not introduce a new product) that will affect the goal

Determine or estimate the payoff (or value) associated with each alternative for

every possible future condition

Estimate the likelihood of each possible future condition for each random

variable Evaluate the alternatives according to the goal or decision criterion, andselect the best alternative

1.1.5 Ability of a good decision maker

- Separating the actual problem from its symptoms

- Clearly articulating the problem to others

- Dealing with complexity and ambiguity.

- Quality assurance and control

- Reliability and maintenance

- Crop protection

- Credit and loan portfolio management

- Project selection

- New product development

- New venture launching

Government

- Emergency management

- Environmental risk management

- Choice of new energy sources

- Research and development programs

Common

- Medical diagnosis and treatment

- Bidding and negotiation

- Litigation

1.2. Decision analysis tool

1.2.1 Decision tree

A decision tree is a graphical representation of the decision variables, randomvariables and their probabilities, and the payoffs The term gets its name from thetree like appearance of the diagram

Decision trees are particularly useful for analyzing situations that involve

sequential or multistage decisions For instance, a manager may initially decide to

build a small facility but she has to allow for the possibility that demand may behigher than anticipated In this case, the manager may plan to make a subsequentdecision on whether to expand or build an additional facility

A decision tree is composed of a number of nodes that have branches

emanating from them Square nodes denote decision points, and circular nodesdenote chance events Read the tree from left to right Branches leaving squarenodes represent alternatives; branches leaving circular nodes represent the states ofnature

After a decision tree has been drawn and necessary data are determined, it is

analyzed from right to left; that is, starting with the last decision that might be made

it is “rolled” back For each decision, choose the alternative that will yield thegreatest return (or the lowest cost) For each chance node, calculate the expectedvalue of the payoffs of its states of nature If chance events follow a decision,choose the alternative that has the highest expected value (or lowest expected cost).The dollar amounts at the branch ends indicate the estimated payoffs if thesequence of decisions and chance events occurs For example, if the initial decision

is to build a small facility and it turns out that demand is low, the payoff will be $40(thousand) Similarly, if a small facility is built, and demand turns out high, and alater decision is made to expand, the payoff will be $55 The figures in parentheses

demand is 0.6 Payoffs in parentheses indicate losses

Figure 1.1 Decision tree fot a facility building

Analyze the decisions ( Figure 1.1) from right to left:

1 Determine which alternative would be selected for each possible second

decision For a small facility with high demand, there are two choices: do nothing,

or expand Because expand has higher payoff, you would choose it Indicate this by placing a double slash through do nothing alternative Similarly, for a large facility with low demand, there are two choices: do nothing or reduce prices You would choose reduce prices because it has the higher expected value, so a double slash is

placed on the other branch

2 Determine the product of the chance probabilities and their respective

payoffs for the remaining branches:

Build small $16 + $33 = $49

Build large $20 + $42 = $62

Hence, the choice should be to build a large facility because it has higher

expected value than the small facility

1.2.2 Influence Diagram

Influence diagrams (see example below) can graphically represent complexdecision problems that have many random variables (chance events) and one ormore decision variables

Influence diagrams are more concise than decision trees because they do notshow the alternatives branches coming out of the decision nodes and the states ofnature branches coming out of the chance nodes

Constructing and validating an influence diagram improves communication andconsensus building at the beginning of the decision modelling process

The following is an example of the influence diagram representing the decision

of whether or not to introduce a new product

The green circles show the random variables (chance events) and the roundedyellow squares show the payoff or part of it This influence diagram for a newproduct decision also involves a pricing decision

The uncertainties (i.e., random variables) are units sold, which are affected bythe pricing decision, fixed cost, and variable cost Profit is the ultimate payoff,which is influenced by the total cost and revenue

Figure 1.2 Influence Diagram of introducing a new product

1.3. Expected Value (EV)

1.3.1 Calculation Expected Value (EV)

The expected value for an uncertain alternative is calculated by multiplyingeach possible outcome of the uncertain alternative by its probability, and summingthe results The expected value decision criterion selects the alternative that has thebest expected value In situations involving profits where “more is better" thealternative with the highest expected value is best, and in situations involving costs,where “less is better" the alternative with the lowest expected value is best

2) for the pressure sensor alternative:

0,8 x $390.000 + 0,2 x ( - $10.000) = $310.0003) for doing neither of these $0

Thus, the alternative with the highest expected value is developing thetemperature sensor, and if the expected value criterion is applied, then thetemperature sensor should be developed

Figure 1.3 Expected value decision in developing temperature sensor

1.3.2 Expected value of perfect information (EVPI)

Expected value of perfect information (EVPI) is the difference between the

Table 1.1 Possible future demand in building a facility

Possible Future Demand

The expected payoff under risk, as calculated in Example S-1, is $10.5 The

EVPI is the difference between these:

EVPI = $12.2 − $10.5 = $1.7

EVPI indicates the upper limit on the amount the decision maker should be

willing to spend to obtain information Thus, if the cost exceeds EVPI, the decision maker would be better off not spending additional money and simply going with thealternative that has the highest expected payoff

1.4 Sensitivity analysis

Sensitivity analysis provides the range of probability over which an alternative hasthe best expected payoff The approach illustrated here is useful when there are twostates of nature It involves constructing a graph and then using algebra to determinethe range of probabilities for which a given alternative is best In effect, the graphprovides a visual indication of the range of probability over which variousalternatives are optimal, and the algebra provides exact values of the endpoints ofthe ranges

Table 1.2 State of Nature

- First, plot the expected payoff of each alternative relative to P2 To do this, plot the

#1 payoff on the left side of the graph and the #2 payoff on the right side Forinstance, for alternative A, plot 4 on the left side of the graph and 12 on the rightside Then, connect these two points with a straight line The three alternatives areplotted on the graph as shown below

- The graph shows the range of values of P2 over which each alternative is optimal

Thus, for low values of P2 (and thus high values of P1, since P 1 + P 2 = 1.0),

alternative B will have the highest expected value; for intermediate values of P2

alternative C is best; and for higher values of P 2 alternative A is best

Figure 1.4 Sensitivity analysis of 3 alternatives

- From the graph, we can see that alternative B is best from the point P2 = 0 to thepoint where the alternative B line intersects the alternative C line To find that point,

solve for the value of P2 at their intersection This requires setting the two equations

equal to each other and solving for P 2 Thus,

16 − 14 P2 = 12 − 4 P2

- Rearranging terms yields

4 = 10 P2

- Solving yields P2 = 0.40 Thus, alternative B is best from P 2 = 0 up to P2 = 0.40

Alternatives B and C are equivalent at P2 = 0.40

- Alternative C is best from that point until its line intersects alternative A’s line To

find that intersection, set those two equations equal and solve for P2 Thus,

4 + 8 P 2 = 12 − 4 P2

- Rearranging terms results in 12 P2 = 8

- Solving yields P2 = 0.67 Thus, alternative C is best from P2 > 0.4 up to P2 = 0.67,

where alternatives A and C are equivalent For values of P2 greater than 0.67 up to

P 2 = 1.0, alternative A is best.

- Table 3 is the summary of equations above

Table 1.3 Equations of 3 alternatives

CHAPTER 2 RECOVERY FACTOR AND RESERVES

2.1 Estimate the recovery factor

The recovery factor for oil is a target for how great a proportion of the oil can be

recovered

Estimate of recoverable oil

Re covery factor =

Estimate of in-place oil

The in-place volumes and the volumes assumed to be recoverable are both used tocalculate the recovery factor Uncertainty is attached to both these quantities,especially in the early phase of a project The various oil companies, moreover,often calculate the in-place volume differently, thus making it difficult to comparethe recovery factor from one field to another Changes in the recovery factor overtime are, nevertheless, an indicator of the effort made by the licensees to enhancerecovery

Drive mechanism has the greatest geological impact on recovery factor Narrowingthe range in recovery factor is a matter of estimating how much difference pore typeand reservoir heterogeneity impact the efficiency of the drive mechanism Toestimate the recovery factor, use the procedure below:

- Decide which drive mechanism is most likely from the geology of theprospective reservoir system and by comparing it with reservoir systems ofnearby analog fields or analog fields in other basins

- Multiply STOIIP or GIIP by the recovery factor for the expected drive

- Narrow the recovery factor range by predicting the thickness of the reservoir

by port type Port type affects recovery rate For example, in a reservoir withstrong water drive and macroporosity, recovery will be up to 60%,mesoporosity recovery will be up to 20%, and microporosity recovery will

RF = RF P + RF S

The primary recovery factor, RFP, is estimated from the type of drive mechanism

Table 2.1 Estimation of primary recovery factor

Figure 2.1 Estimating recovery factor by analogue

These efficiency terms are influenced by such factors as residual oil saturation,relative permeability, reservoir heterogeneity, and operational limitations thatgovern reservoir production and management Thus, it is difficult to calculate therecovery factor directly using these terms, and other methods, such as declinecurves, are often applied

Because of reservoir characteristics and limitations in petroleum extractiontechnologies, only a fraction of this oil can be brought to the surface, and it is onlythis producible fraction that is considered to be reserves

Because the geology of the subsurface cannot be examined directly, indirecttechniques must be used to estimate the size and recoverability of the resource.While new technologies have increased the accuracy of these techniques, significantuncertainties still remain In general, most early estimates of the reserves of an oilfield are conservative and tend to grow with time This phenomenon is calledreserves growth.

2.2.1.2 Classification

All reserve estimates involve uncertainty, depending on the amount of reliablegeologic and engineering data available and the interpretation of those data Therelative degree of uncertainty can be expressed by dividing reserves into twoprincipal classifications—"proven" (or "proved") and "unproven" (or "unproved")

Figure 2.2 Schematic graph illustrating petroleum volumes and probabilities

a) Proven Reserves

Proven reserves are those reserves claimed to have a reasonable certainty(normally at least 90% confidence) of being recoverable under existing economic

Probable reserves are attributed to known accumulations and claim a 50%confidence level of recovery Industry specialists refer to them as "P50" (i.e., having

a 50% certainty of being produced)

Possible reserves are attributed to known accumulations that have a lesslikely chance of being recovered than probable reserves This term is often used forreserves which are claimed to have at least a 10% certainty of being produced("P10") Reasons for classifying reserves as possible include varying interpretations

of geology, reserves not producible at commercial rates, uncertainty due to reserveinfill (seepage from adjacent areas) and projected reserves based on future recoverymethods

2.2.2 The methods are used to estimate reserves

Estimating hydrocarbon reserves is a complex process that involves integratinggeological and engineering data Depending on the amount and quality of dataavailable, one or more of the following methods may be used to estimate reserves:

- Volumetric

- Material balance

- Production history

- Analogy

Table 2.2 The methods are used to estimate reserves

Volumetric STOIIP, GIIP, recoverable reserves

Use early in life of field Dependent on quality of reservoir description

Reserves estimates often highbecause this method does notconsider problems of

(assumes STOIIP and GIIP known)

Use in a mature field with abundant geological, petrophysical, and engineering data

Highly dependent on quality

of reservoir description and amount of production data available Reserve estimates variable

is available Reserve estimates tend to be

realistic

Analogy STOIIP, GIIP, recoverable reserves

Use early in exploration and initial field development

Highly dependent on similarity of reservoir characteristics Reserve estimates are often very general

2.2.2.1 Volumetric estimation

Volumetric estimates of STOIIP and GIIP are based on a geological modelthat geometrically describes the volume of hydrocarbons in the reservoir However,due mainly to gas evolving from the oil as pressure and temperature are decreased,oil at the surface occupies less space than it does in the subsurface Conversely, gas

at the surface occupies more space than it does in the subsurface because ofexpansion This necessitates correcting subsurface volumes to standard units ofvolume measured at surface conditions

N = STIIP (STB)

A = area of reservoir (ft2) from map data

h = height or thickness of pay zone (ft) from log and/or core data

N/G = net to gross ratio

Φ = porosity (decimal) from log and/or core data

Sw = connate water saturation (decimal) from log and/or core data

Boi = formation volume factor for oil at initial conditions (reservoir bbl/STB)

from lab data; a quick estimate is Boil = 1.05+(N x 0.05), where N is the number of

hundreds of ft3 of gas produced per bbl of oil [for example, in a well with a GOR of

1000, Boi = 1.05 + (10 × 0.05)]

Another basic volumetric equation is

w gi(1 S ) / B

Bgi = formation volume factor for gas at initial conditions (RES ft3/SCF)

Recoverable reserves are a fraction of the STOIIP or GIIP and are dependent

on the efficiency of the reservoir drive mechanism The basic equation used tocalculate recoverable oil reserves is

Recoverable oil reserves or Ultimate recovery (STB) = HCIIP x RF

2.2.2.2 Material balance estimation for oil

The material balance technique mathematically models the reservoir as a tank.This method uses limiting assumptions and attempts to equilibrate changes inreservoir volume as a result of production Aquifer support and gas cap expansioncan be accounted for by using this method

One general equation is:

Change in pore volume = Change in oil volume + change in free gas volume

+ change in water volume

NB

c P

−

Change in oil volume = NB oi −(N N ) B− p oi

Change in gas volume = (GB gi−GB g)+ N R p p(N N ) NR − p − si B g

Change in water volume = (1 S )wi

where

Bg = formation volume factor of free gas

Bgi = formation volume factor of free gas at initial conditions

cf = formation (rock) compressibility (psi–1)

cw = water compressibility (psi–1)

N = STOIIP (STB)

Np = cumulative oil produced (STB); from production history data

P = Change in reservoir pressure due to production, that is, initial pressure

Wp = cumulative water produced; from production history data

Another general equation is

Bt = total (two-phase) formation volume factor

Bti = total formation volume factor at initial conditions

M = gas cap size expressed as a fraction of initial reservoir oil volume; from

map data

This equation assumes thermodynamic equilibrium between oil and gas, auniform pressure distribution, and a uniform saturation distribution in the reservoir.Additional equations can be derived from the general material balance equation forspecific reservoir types

2.2.2.3 Production history analysis

Production history analysis is used to estimate economic ultimate recovery (orrecoverable reserves) and the expected economic life of a reservoir The rate ofproduction and cumulative production at any point in time can also be estimated.This method relies on historical production data to extrapolate future productionperformance

Figure 2.4 Production history curves

Figure 2.6 Relationship of decline behavior to decline curve characteristics

Three mathematical models can be used to describe decline curve (usually rateversus time) behavior They are:

Table 2.3 Decline equations

q t

q t

= ÷ −

qt = Rate of production at time t (BOPD)

qi = Rate of initial production (BOPD)

qec = Economic limit rate of production (BOPD)

D = Decine rate (decimal)

Di = Initial decline rate (decimal)

t = Time (years)

n = Exponent usually between 0 and 0.7

Np = Cumulative production (STBO)

2.2.2.4 Analogy method

The analogy method for estimating reserves directly compares a newlydiscovered or poorly defined reservoir to a known reservoir thought to have similargeological or petrophysical properties (depth, lithology, porosity, and so on) Whileanalogy is the least accurate of the methods presented, it is often used early in thelife of a reservoir to establish an order-of-magnitude recovery estimate As the fieldmatures and data become available to make volumetric STOIIP or GIIP estimates,analogy is often used to establish a range of recovery factors to apply to the in-placevolumes Evaluating recovery in this fashion is particularly useful when someperformance history is available but a decline rate has yet to be established.Analogy should always be used in conjunction with other techniques to ensure thatthe results of the more computationally intensive methods make sense within thegeological framework

2.3 Expectation curves for a discovery

Figure 2.7 Expection curve for a discovery

The percentages chosen are often denoted as the p85, p50, p l 5 values Becausethey each approximately represent one third of the distribution, their discreteprobabilities may each be assigned as one third This approximation is truefor a normal (or symmetrical) PDF (probability density function)

If the whole range is to be represented by just one value (which of coursegives no indication of the range of uncertainty), then the "expectation value" isused:

An alternative and commonly used representation of the range of reserves is theproven, proven plus probable, and proven plus probable plus possibledefinition The exact cumulative probability which these definitions correspond

to on the expectation curve for Ultimate Recovery varies from country tocountry, and sometimes from company to company However, it is always truethat the values lie within the following ranges:

proven : between 100% and 66%

proven + probable : between 66% and 33%

proven + probable + possible : between 33% and 0%