Feasibility Study of Various Litho-Fluid Indicators for ...

Feasibility Study of Various Litho-Fluid Indicators for Better Hydrocarbon Prediction in Malay and adjacent Basin Syaza Zuhaira Shamsuddin, Maman Hermana, Deva Ghosh and Ahmed Mohamed Ahmed Salim. Department of Geosciences and Petroleum Engineering, Universiti Teknologi PETRONAS

Abstract- The application of lithology and fluid indicators has helped the geophysicists to discriminate reservoirs to non-reservoirs from a field. This analysis is conducted to select the most suitable lithology and fluid indicator for the Malay basin that could lead to better eliminate pitfalls of amplitude. This paper uses different rock physics analysis such as Poisson’s impedance, acoustic impedance, shear impedance, elastic impedance, LambdaMuRho (LMR), and attribute. Poisson’s impedance (PI) log is generated by using the method by determining the constant from AI-SI gradient. Then, litho-elastic impedance log is generated by correlating the gamma ray log with extended elastic impedance (EEI) log. The same application is used for fluid-elastic impedance by correlation of EEI log with water saturation or resistivity. The work is done on several well logging data collected from different fields in Malay basin and its neighbouring basin. Preliminary observations are done on three wells; Well-S3, Well-B1 and Well-K1. The constant, c in PI log for Well-S3 is 1.305 and 1.58 for Well-K1. There’s an excellent separation between hydrocarbon sand and background shale for Well-K1 from different cross-plot analysis. The similar method is done on the Well-S3 shows fair separation of silty sand and gas sand using SQp-SQs attribute which can be correlated with well log. Meanwhile, the third well shows good separation in LMR plot. Based on the point distribution histogram plot, different lithology and fluid can be separated clearly. There are many attributes available in the industry used to separate the lithology and fluid, however some of the methods are not suitable for the application to the basins in Malaysia. Keywords- Litho-fluid indicator, Poisson Impedance, SQp-SQs, EEI

I. INTRODUCTION Malaysia has been producing hydrocarbons from various fields since the early 1900s, with the first field was found in onshore Miri, Sarawak. The following major discoveries were made offshore in Sarawak, Sabah and Malay Basins. The location of Malay basin is at the eastern side of Peninsular Malaysia and surrounded by Pattani basin at north, West Natuna basin at south, Sarawak and Sabah basin at its southeastern part. Malay basin is known to be an extensional pull-apart basin with anticlines trending from east to west direction including series of half-grabens. Primarily, the basin formed during the late Eocene to early Oligocene, followed by thermal subsidence leading to sedimentation in the early Miocene. Later in the middle Miocene, regional stress fields changed and the basin inverted forming east-west anticlines trend (Ghosh et. al, 2010). One of the many geophysical issues faced by the interpreters is the pitfalls of amplitudes, for examples are coal, soft shale, and high-porosity brine sand. Coal has been the excellent source rock for Malay basin reservoirs and good horizon markers, however the presence of coal masks interfere the reservoirs underneath by forming strong soft negative impedance. This is due to the acoustic properties of coal that has low density and low velocity, which shows similar acoustic impedance response to the gas sand. Another issue is that good porosity brine sand shows similar amplitude versus offset (AVO) response to hydrocarbon sand. In the case of poor quality gas sand, the reflectivity shows positive impedance which might be misguided as nonreservoir (Ghosh & Brewer, 2007). In addition, soft shale that composed of unconsolidated rocks (especially younger sediments), produces lower acoustic impedance than the surrounding rocks; similar response as hydrocarbon sand. It is difficult to differentiate the amplitude anomalies based on

acoustic impedance alone, whether they are caused by the lithology or fluid content The main objective of this paper is to choose the best or the most suitable lithology and fluid indicator for Malay basin that could lead to better interpretation of its hydrocarbon reservoirs.

B. Extended Elastic Impedance Whitcombe, (2002) extended the elastic impedance (EI) theory to maximize the separation of fluid or lithologies by introducing the angle chi, 𝟀. The normalization of elastic impedance equation, where the angle 𝞠 is replaced by 𝟀 is shown in Equation 2. (𝟀)

II. METHODOLOGY This paper addresses different application of rock physics analysis such as acoustic and shear impedance, Poisson’s impedance, extended elastic impedance, Poisson ratio, attribute and Lambda-Mu-Rho (LMR). The work is done on several well logging data collected from different fields in Malay basin and its adjacent basin. In the beginning, various logs are generated using different rock physics methods. Later, cross-plot analyses are done using logs to observe which attributes can separate lithology and fluid better. The cross-plotting analysis and EEI correlation is done at certain depth (including reservoir target) to omit the effect of depth trend.

(

) (

Where

,

) ( )

(2)

,

The EEI logs are correlated with gamma ray log to produce EEI_litho log and resistivity or water saturation log to obtain EEI_fluid log. C. SSQp-SSQs These attributes are derived based on attenuation concept –rock physics approximation: [( ⁄ )

]

[( ⁄ )

]

(3)

AI & SI ⁄

Poisson Impedance

EEI_LITHO

GR Log

EEI_FLUID

/ Log

[ ( ⁄ )

]

(4)

correlate

EEI logs

Rock Physics Analysis

Poisson Ratio

Qp-Qs

Correlation between logs

LMR

Cross-plotting

Figure 1: The workflow for the project.

A. Poisson Impedance The Poisson impedance analysis is adopted from the method introduced by Quakenbush et. al, (2006). The method uses constant, c derived from the gradient of AI versus SI plot in order to obtain poisson impedance value based on Equation 1. (1)

The Poisson impedance log is generated for two constant values; 1.41 (theoretical) and derived from well to compare the suitable poisson impedance for the well.

is defined as Scaled inverse Quality factor of pwave which is used as lithology indicator, while is used as pore fill indicator. From Equation 3 and Equation 4, the M, G and ρ represent bulk modulus, shear modulus and density respectively. The M/G is approximates as ( ⁄ ) .

III. RESULTS Three wells are used for the observations; Well-S3, Well B-1 and Well-K1. Overall, PI, , , and EEI logs are the logs which are not affected by depth trend as shown in Figure 2. The example from WellK1 shows that the attribute manages to imitate the gamma ray log but with high value of coal, while attribute is similar to the true resistivity log (Figure 3) In case of PI log, Well-S3 has constant value of 1.305, which is almost similar constant value to the theoretical value, 1.41. However, Well-K1 shows better impedance contrast when ‘c’ value is 1.58. There’s an excellent separation between

GR

AI

Mu-Rho

PI

Vp/Vs

EI

Qs

EI_Litho vs. EI_Fluid

EI_Fluid

hydrocarbon sand and background shale from several cross-plots generated. In Figure 4, the correlated EEI cross-plot shows good separation of gas sand from shale, which can be difficult to differentiate from logs only. The gas sand cluster is represented by the combination of low EI_litho and EI_fluid values. Well-S3 lithology can be distinguished the best using the attributes, where the hydrocarbon sand cluster is deviated away from the wet trend from the cross-plot in Figure 5. In case of Well-B1, LMR crossplot manages to separate clearly the gas sand from the brine sand and shale. The gas sand cluster isrepresented by the low to high values of Mu-Rho and low values of Lambda-Rho (Figure 6).

SHALE

GAS SAND

EI_Litho

Figure 4: Cross-plot of EEI for Well-K1 shows good separation between gas sand and background trend. Water saturation log as the colour key. Qs vs Qp

Qs

Silty Sand

Gas Sand

Figure 2: Logs from Well-S showing that PI, EI and SQs logs do not show presence of compaction trend. Qp

GR

VSHALE

Well-K1 QP-1

LLD

QS-1

Figure 5: Cross-plot of SQp vs SQs for Well-S3 separates gas sand from silty sand. The colour key represents the resistivity at reservoir target. LMR

Brine sand

μρ

HC sand

Shale

Figure 3: Comparison of SQp-1 and SQs-1 logs in well-K1 with gamma ray log and resistivity logs respectively. λρ

Figure 6: LMR crossplot from Well-B1 shows the hydrocarbon sand cluster separates from the wet one and shale. The colour key represents the resistivity values.

A

B

C

D

Figure 7: Plot A represents the frequency of Poisson Ratio distribution, B represents Lambda-Rho, C displays the plot of elastic impedance at 20˚ angle, and D shows distribution of SQs points. On the right side of each histogram shows the lithology type at well location with GR, resistivity and elastic parameters log (from left to right in order).

The occurrence of different lithology can be determined much clearer using the histogram plot. By assuming the points are distributed normally, each lithology can be defined based on the distribution curves trends. In the case of Poisson Ratio in Figure 7(A), the curves can be grouped into three different lithology; gas sand, silty sand and shale. This case is applicable for Lambda-Rho (λρ) and elastic impedance (EI) distributions (B and C respectively) with three lithology types can be defined. However, both EI and λρ do not show distinct separation between silty sand (yellow) and shale (green), as silty sand and shale curves might overlap with each other. Hence, it is possible for other interpreters to group the lithology into two classes only. Meanwhile, SQs attribute presents four clear distribution curves. From the well display on its right, lithology can be categorized separated into shale (grey), poor silty sand (yellow), silty sand (green) and gas sand.

IV. CONCLUSIONS There are various methods available to delineate reservoir from non-reservoir. However, the methods are not applicable to all fields worldwide. For the

shale-dominated well or well that has distinct lithology variables, it would be convenient to use conventional rock physics methods to separate the lithology and fluid. On the other hand, if the well is dominated by silty sand, it is difficult to distinguish the pay sand based on the available rock physics methods only. The analyses suggest that the most suitable method for the silty sand-dominated formation is the SQp-SQs attribute as they manage to display clear separation of hydrocarbon sand from the background trend and different lithology The LMR method is able to distinguish clearly the gas and brine sand from shale-dominated formation. This analysis explained in this paper is based on well-domain only. However, implementation of inversion of pre-stack seismic data could further analyze the effect of selected methods on larger scale.

V. REFERENCES Connolly, P. (1999, April). Elastic Impedance. The Leading Edge, pp. 438-452. Ghosh, D. P., & Brewer, M. (2007). Interpretation of Amplitudes: Pitfalls & Lessons Learnt. PETRONAS CARIGALI. Ghosh, D., Abdul Halim, M. F., Brewer, M., Viratno, B., & Darman, N. (2010, April). Geophysical issues and challenges in Malay and adjacent basins from an E&P perspective. The Leading Edge, pp. 436-449. Quakenbush, M., Shang, B., & Tuttle, C. (2006, February). Poisson Impedance. The Leading Edge, pp. 128-138. Whitcombe, D. N. (2002). Elastic Impedance Normalization. Geophysics, 60-62. Yao, Q., & Han, D.-h. (2008). Acoustic properties of coal from lab measurement. SEG Las Vegas 2008 Annual Meeting (pp. 1815-1819). Nevada: Society of Exploration Geophysics.