Prospect Evaluation in Marginal Reservoirs by Integration of … · 2020. 4. 14. · single buildup...

3rd SPWLA-INDIA Symposium, Mumbai, India Nov 25-26, 2011

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Prospect Evaluation in Marginal Reservoirs by Integration of

Formation Tester Interval Pressure Transient Tests and Well

Performance Analysis: A Case Study from Onshore India

Rao, K.S1; Murthy, K.S

1; Tellapaneni, P.K

2; Ojha, A.

2; Jackson, R. R.

2; Nahar, S

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1 Oil and Natural Gas Corporation Ltd. 2 Schlumberger

Copyright 2011, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors.

This paper was prepared for presentation at the SPWLA 3rd Annual Logging Symposium held in Mumbai, India November 25-26, 2011.

ABSTRACT

Field development decision making increasingly needs input from cost-effective technologies and methods to

reliably evaluate reservoir prospects and assess well productivity in undeveloped or marginal reservoirs.

Conventional production or drillstem tests (DSTs) have traditionally been used by operators to decide the

commercial viability of any new prospect. However, in marginal reservoirs, conventional testing carries the risk of

inconclusive or unreliable operations. Using wireline-conveyed formation testers to conduct interval pressure

transient tests (IPTTs) provides dynamic reservoir information and is increasingly employed for mitigating risks

associated with conventional well testing operations.

We describe, by the aid of a case study, the utility of IPTT and analysis of pressure transient data for the assessment

of well productivity and flow potential. Absolute open-flow potential (AOFP) is determined by analyzing pressure

buildup data using a single-point inflow performance relationship (IPR) method. The results are consistent with

analysis of flow-after-flow (FAF) tests performed during these operations. With this formation dynamics

information, a conventional test is simulated to access the risks associated with a conventional test in terms of

effective wellbore and formation cleanup. Various sensitivities were performed on the fluid type and stimulation

scenarios. For some well test scenarios, although the well would flow to surface, production cleanup would be

incomplete even after relatively long flow periods, rendering planned well tests suboptimal and potentially

producing inconclusive or low-confidence results.

Integrating this evaluation methodology in any exploratory workflow can help improve prospect evaluation and

optimize testing operations, leading to potential cost savings for the operator and a better decision-making process

for field development.


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INTRODUCTION

The Krishna Godavari basin (Fig. 1) in India has been under hydrocarbon exploration since late 1950s, but it was

only after 1978 when an increase in exploration activity led to several oil and gas discoveries.

Fig.1: Geographical location of the Krishna Godavari basin

The Krishna-Godavari basin is a proven petroliferous basin with commercial hydrocarbon accumulations in the

oldest deposits of Permian-Triassic Mandapeta sandstone on land to the youngest Pleistocene channel levee

complexes in deep water offshore. The basin is endowed with four petroleum systems, which can be classified

broadly into two categories, Pre-Trappean and Post-Trappean, in view of their distinct tectonic and sedimentary

characteristics. The basin is an established hydrocarbon province with a resource base of 1130 MMT, of which, 575

MMT has been assessed for the onshore region.

Owing to the continuously increasing gap between the demand for and supply of hydrocarbons in India and the huge

potential of the basin, exploration activities are at an all-time high in the region. Under the aggressive exploration

drive by the operator, Well A, an exploration Class „B‟ well, was drilled vertically with an objective to explore

sands with in Eocene (Pasarlapudi Formation) and Paleocene (Palakollu Formation) sequences. Petrophysical

evaluation showed development of a number of sand packs within this well, but the zone of interest was limited to a

4-m interval between XX08 m and XX12m. Formation evaluation was then conducted to identify the resource

potential of this sand.

WORKFLOW

Initial Evaluation. Initial formation evaluation was done by a triple-combo run in Well A, including gamma-ray,

laterolog resistivity, and density-porosity measurements (Fig. 2). Initial log analysis showed a 4-m sand

development (XX08 m–XX12 m), out of which a 1-m interval (XX09 m–XX10 m) displaying good porosity was

selected for further evaluation.


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Fig. 2: Basic petrophysical logs delineating the zone of interest

Wireline Formation Tester Pressure and Fluid Identification

Based on the openhole log evaluation, it was decided to acquire pressure and fluid information in the selected zone

of interest by means of a wireline formation tester (WFT). Pressures in the zone of interest were found to be higher

than the expected hydrostatic gradient, with mobilities ranging from 0.36 to 17.6 mD/cP. Fluid identification using a

resistivity cell showed a sharp increase in the flowline resistivity response over the course of pumping along with a

pronounced scattering effect, which suggested the possibility of hydrocarbons. A downhole sample was collected

after pumping 22 liters of formation fluid (Fig. 3). Surface draining of the sample showed it to be predominantly

gas. A resistivity cell works on the principle of potential difference measured across electrodes and the measurement

is dominated by the continuous phase. Therefore, in certain hydrocarbon-water scenarios, the measurement might

not discriminate between a continuous phase of hydrocarbons with water as the dispersed phase and pure

hydrocarbon. Also, the electrodes can be wetted by a preferential phase and therefore may respond to the wetting

phase and may not respond to the fluids we might be interested in. Therefore, sampling with a resistivity cell alone

may provide “surprises” when samples are drained on surface. The presence of a downhole optical fluid analyzer

would have proved advantageous here because the contamination could be monitored and a better sample could

have been collected.

Fig. 3: Pressure and Resistivity versus Time plot

Although the presence of hydrocarbon was confirmed by analysis of the acquired data, the main questions to be

answered were

1. Is the 1-m zone worth investing money on initial casing and the completions required for testing?

2. Would this zone be productive enough to be declared as a commercial discovery?

To this end, it was decided to do an interval pressure transient test (IPTT) to determine well deliverability.


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Interval Pressure Transient Testing

Conventionally, drillstem tests (DSTs) are used to prove the commercial viability of a reserve. The important well

test objectives may be grouped into the following four categories:

Initial reservoir pressure

Skin (quality of completion)

Permeability and zone deliverability

Fluid properties and distribution

But prior to investing millions of dollars in a costly well test operation, it is important to analyze the extent to which

the well test objectives can be realized versus the cost involved. As a result, it was decided to use IPTT as a „go–no

go‟ for the DST (Samuelson et al., 2009)

An IPTT is the interpretation of the pressure transients generated by the production of the reservoir fluids from a

particular zone straddled between a pair of packers. Typically an IPTT is conducted using wireline-conveyed

formation testers that include straddle packers, downhole pumps, and optical fluid analyzers in the string. When at

the zone of interest, the straddle packers are inflated using downhole pumps. Upon inflation, a 1-m interval is

isolated from the wellbore. Using downhole pumps, the formation fluids are produced from the interval and cleanup

is monitored by the downhole fluid analyzer. Once representative formation fluids start to flow, the pumps are

stopped and a pressure buildup is recorded until a radial flow regime is observed. Using various analytical models,

the pressure transients during the drawdown and buildup are analyzed to estimate reservoir parameters such as

permeability and skin. These estimates can be used to calculate the productivity potential of the zone (Fig. 4).

Fig.4: IPTT interpretation workflow

IPTT Analysis in Well A. As mentioned previously, a 1-m zone was selected for an IPTT (XX09 m–XX10 m) based

on mobility and porosity characteristics. The main objective of performing IPTT analysis was to estimate the

absolute openhole flow potential (AOFP) to gauge deliverability and thus the commercial viability of the well.

The toolstring for the operation consisted of a set of straddle packers, an optical fluid analyzer, pumpout module,

and sample chambers for the downhole collection of fluid samples (Fig. 5).

The 1-m zone was isolated using the straddle packers. After packer inflation, a small-volume pretest was performed

using the pumpout module followed by an extended cleanup and main flow period at a constant downhole flow rate.

Downhole optical fluid analyzer response showed a gas breakthrough after 1 hour of pumping.


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Fig. 5: IPTT toolstring

Continued pumping showed an increasing gas fraction with time (Fig. 6). After flowing for 1.5 hours and pumping

120 litres, flow was stopped to record a main buildup, which stabilized to a pressure of ~7058 psia. The buildup was

followed by a flow-after-flow test during which the fluid was pumped at various rates equivalent to the downhole

rates of 6.1, 9.6, and 13.7 B/D. This was succeeded by a final buildup that stabilized at a pressure of 7058.8 psia

(Fig. 7).

Fig. 6: Optical fluid analyzer response with time


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Fig. 7: Pressure versus time plot of IPTT station

IPTT Interpretation Results

The buildup periods for the station were analyzed using a limited-entry vertical well model in an infinite-acting

homogenous reservoir (Fig. 8).

Fig. 8: Pressure Time plot of IPTT station

Only the buildup periods were analyzed, because the flow period was noise affected. Both the main and final

buildup pressure derivatives are consistent (Fig. 9) and are expected to show comparable results. Hence, only the

final buildup was used for analysis.

Fig. 9: Comparison of main and final buildup derivatives

Based on the log-log flow regime analysis plot of the-pressure and-pressure derivative shown in Fig. 10, an infinite-

acting radial flow regime (IARF) was identified.

0.1 1 10 100 10001E+5

1E+6

1E+7

FinalBuildUp (ref)

MainBuildUp

Log-Log plot: dm(p) and dm(p)' normalized [psi2/cp] vs dt


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Fig. 10: Log-log plot showing limited entry effects and IARF

After identification of IARF, an analytical model (black line) was used to describe the observed pressure derivative

trend (red dots). A standard vertical well model was chosen with an infinite homogeneous reservoir and constant

wellbore storage. As can be seen in Fig. 11, the modelled pressure history (observed data in blue; fitted model in

red) shows a good match to the actual pressure, thus validating the pressure derivative fit.

Fig. 11: Pressure derivative and history match

Absolute Open Flow Potential computation. Using the modeled results from the transient interpretation described

previously, AOFP was estimated by two methods, which are described as follows.

i) Single-point AOFP estimation: For using the single-point AOFP estimation, the following parameters are

required:

Average formation pressure

Net hydrocarbon pay thickness and net pay thickness based on permeability and porosity

Fluid properties

External radius


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AOFP is estimated based upon the reservoir parameter (flow capacity) and skin obtained from transient analysis of a

single buildup period. Hence the technique is called „single-point‟ AOFP estimation, also referred as „Darcy IPR‟.

The expression used for estimating AOFP is the pseudo pressure approximation to the pseudo steady-state flow of

gas (Lee and Wattenbargar, 1996), which is as follows (in oilfield units):

MMSCF/D (1)

Reducing Equation 1 in quadratic form and solving for the positive root gives the AOFP (Karthik et al., 2008; Lee,

1982).

In this case, AOFP was calculated with and without rate-dependent skin and zero formation damage skin. As we

were dealing with a gas bearing formation, the AOFP values determined using the rate dependent skin was

considered more realistic.

ii) C & n method: The C & n method for gas case is described in the following equation:

(2)

Three flowing pressures and corresponding flow rates in the flow after flow periods (Fig. 12) were used to estimate

AOFP. Plotting the pressures and flow rates on a log-log plot, values of C and n are estimated as shown in Fig. 12.

Fig. 12: Estimating C and n using log-log plot

The AOFP values determined independently from both computation methods as mentioned above were comparable

and the flow potential was deemed acceptable to declare the well a commercial discovery. But did the well have

enough fluid influx for an effective cleanup? To address this, further investigation was conducted using numerical

simulators to assess the cleanup profiles.


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NUMERICAL SIMULATION (Andre et al, 2005) OF PRODUCTION SCENARIOS

To predict the well behaviour during DSTs, numerical simulations were conducted for different production

scenarios. Table 4 outlines the grid generation and wellbore model creation (Fig. 13) parameters:

Modeled interval 20-m interval (XX00 m–

XX20 m)

Grid 51 × 51 × 40

Production packer setting

depth

XY90 m

Tubing shoe depth XX00 m

Perforated interval XX09.3 m–XX10.3 m

Pay thickness 1.0 m

Depth of invasion 10 ft

Table 4: Simulation grid and wellbore model parameters

Fig. 13: Simulation grid and wellbore model generation

Formation and fluid parameters input into the model are outlined in Table 5. These parameters were used to derive

relative permeability and formation volume factor curves.

Reservoir pressure 7060 psia

Reservoir temperature 234 degF

Fluid type Gas

Gas gravity 0.65

Gas viscosity 0.031cp

Rock type Sandstone

Effective porosity 20%

Connate water saturation 30%

Table 5: Formation parameters

Once the relevant parameters were input into the model, well performance was simulated by initially flowing the

well at a fixed choke size. It was observed that the tubing cushion fluid started offloading without any gas influx

even after 10 hours of flow with a corresponding drop in pressure. The choke size was then doubled which resulted

in gas breakthrough. However, even after a considerable period of time, the wellbore volume could not be

completely offloaded and no stabilization was observed in the gas rate (Fig. 14). Pressure transient analysis cannot

be performed in such a scenario leading to inconclusive results. In comparison, the IPTT analysis is not influenced

by well offloading effects since flow rate is measured downhole along with the pressures.


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Fig. 14: Simulation of wellbore cleanup

To understand the performance of the well under damaged/stimulated conditions, various sensitivities were

simulated under positive and negative skin influences. It was observed (Fig. 15) that well performance decreased

drastically with damage (positive skin) while no significant improvement in the cleanup response could be observed

after stimulation (negative skin).

Fig. 15: Well performance simulated under various damaged/stimulated scenarios

To understand the significance of the accurate knowledge of the fluid PVT properties, sensitivities were attempted to

compare wellbore cleanup profiles for dry gas, light oil, and wet gas (condensate/gas ratio (CGR): 0.2 STB/Mscf).


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A significant reduction in well performance was observed with the increase of heavier fractions in the formation

fluid (Figs. 16 and 17).

Fig. 16: Well performance comparison between dry gas and light oil

Fig. 17: Well performance comparison between dry gas and wet gas

Using the workflow described in the paper, it was ascertained that attempting a full-scale DST in the well would not

yield conclusive results owing to the ineffective cleanup, even after long flow periods. Based on the results, the

operator decided not to go ahead with a DST, thus saving the substantial investments required for the initial

completion and casing.

CONCLUSION

Increasing hydrocarbon demand has pushed the exploration drive to new frontiers involving the tapping of resources

in low-quality reservoirs, which were previously deemed uneconomical. An early assessment of the reservoir fluids

and potential production is imperative to minimize the economic risks in such expeditions. The completion decisions

have historically been based on the results of well testing, but low-permeability well tests may not be economical

and may not provide conclusive results.

In an exploratory scenario, the presented workflow can be integrated to the prospect evaluation strategy, which can

lead to an intelligent optimization of the well testing plans (Whittle et al., 2003). This will lead to potential cost

savings and a better field development plan.


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ACKNOWLEDGMENTS

The authors wish to thank Oil and Natural Gas Corporation Ltd. and Schlumberger for permission to publish this

paper.

NOMENCLATURE

C = wellbore fluid compressibility, bbl/psi

hw = perforated zone thickness, ft

zw = distance from midpoint of perforation to the bottom of zone, ft

Pi = initial reservoir pressure, psia

k = permeability, mD

kz/kr = permeability anisotropy

q = AOFP, Mscf/D

kh = flow capacity, mD.ft

Pres = reservoir pressure, psia

= gas viscosity, cP

T = reservoir temperature, R

Z = gas compressibility factor

re = drainage area radius, ft (1177.5ft ~ 358 m for 100-acre drainage area)

S = skin factor

D = rate dependent skin, (Mscf/D) -1

= average reservoir pressure, psia

pwf = well flowing pressure, psia

REFERENCES

Samuelsen, H.E., Gisolf A., et al, „Extending the Limit: Interval Pressure Transient Testing in Low-Permeability

Reservoirs in the North Sea‟; SPE 124842, SPE Annual Technical Conference and Exhibition, New Orleans,

Louisiana, USA, 4-7 Oct, 2009

Lee, J.; Wattenbargar, R.A., „Gas Reservoir Engineering‟, Richardson: SPE Textbook Series, Volume 5; 1996

Karthik, K.N.; Joshi, S., et al., , A new method for gas well deliverability potential estimation using miniDST and

single well modeling – Theory & examples, SPE 113650, Indian Oil and Gas Technical Conference, Mumbai, India,

46 March,2008

Lee. J, , Well Testing, Dallas: SPE Textbook Series, 1982

Andre, C.de; Canas,J.A.,„ Rigorous Approach for Viscous-Oil Productivity Forecast Before Well Completion‟-

SPE-94837, SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janerio, Brazil,20-23

June,2005

Whittle, T.M.; Lee, J., et al, „Will Wireline Formation Testers Replace Well Tests?‟ SPE 84086 , SPE Annual

Technical Conference and Exhibition, Denver, Colorado, USA, 5-8 October, 2003

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