Drilling success as a result of probabilistic lithology and fluid prediction—a case study in the Carnarvon Basin, WA

2008 ◽  
Vol 48 (1) ◽  
pp. 31 ◽  
Author(s):  
Matthew Lamont ◽  
Troy Thompson ◽  
Carlo Bevilacqua
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Geological Model ◽  
Probabilistic Prediction ◽  
Individual Probability ◽  
Fluid Properties ◽  
Lithology Prediction ◽  
Well Data ◽  
Carnarvon Basin

The aim of quantitative interpretation (QI) is to predict lithology and fluid content away from the well bore. This process should make use of all available data, not well and seismic data in isolation. Geological insight contributes to the selection of meaningful seismic attributes and the derivation of valid inversion products. Uncertainty must be taken into account at all stages to permit risk assessment and foster confidence in the predictions. The use of the Bayesian framework enables prior knowledge, such as a geological model, to be incorporated into a probabilistic prediction, which captures uncertainty and quantifies risk. Nostradamus is a fluid and lithology prediction toolkit that forms part of a comprehensive QI workflow. It utilises a Bayesian classification scheme to make quantitative predictions based upon inverted seismic data and depth-dependent, stochastic rock physics models. The process generates lithology and fluid probability volumes. All available information is combined using geological knowledge to create a realistic pre-drill model. Separately, stochastically modelled multidimensional crossplots, which account for the uncertainty in the rock and fluid properties (based on petrophysical analyses of well data), are used to build probability density functions such as acoustic impedance (AI) vs Vp/Vs and LambdaRho vs MuRho. These are then compared to crossplots of equivalent inverted data to make predictions and quantitatively update the geological model. Individual probability volumes as well as a most-likely lithology and fluid volume are generated. This paper presents a case study in the Carnarvon Basin that successfully predicts fluids and lithologies away from well control in a way that effectively quantifies risk and reserves. Two of the three successful gas exploration wells were drilled close to dry holes.

Seismic integrated study for offshore deep- and thin-stratum characterization: A case study from the Enping sag within the Pearl River Mouth Basin

2020 ◽  
Vol 39 (3) ◽  
pp. 176-181
Author(s):  
Jun Liu ◽  
Donghai Liang ◽  
Guangrong Peng ◽  
Xiaomin Ruan ◽  
Yingwei Li ◽  
...  
Keyword(s):  
Seismic Data ◽  
River Mouth ◽  
Rock Physics ◽  
Pearl River ◽  
Pearl River Mouth Basin ◽  
Avo Inversion ◽  
Lithology Prediction ◽  
The Pearl River ◽  
Well Data

In the Enping 17 sag within the Pearl River Mouth Basin in the South China Sea, one wildcat well has been drilled to the Lower Paleogene Enping Formation (FM EP) and partially into the Wenchang Formation (FM WC) for deep formation hydrocarbon exploration. However, no commercial play was discovered. The reasons for this are clear if the petroleum systems modeling is examined. In FM EP, the main reason for failure is due to poor sealing. In FM WC, the failure is due to the lack of a good reservoir for hydrocarbon accumulation. Encountering a 9 m thick reservoir at a depth of 4650 m indicates that braided fluvial delta and lowstand turbidite sandstone may develop in FM WC. With the objective of establishing cap rock in FM EP and reservoir rock in FM WC, and in the absence of sufficient well data, an integrated framework for 3D seismic reservoir characterization of offshore deep and thin layers was developed. The workflow includes seismic data reprocessing, well-log-based rock-physics analysis, seismic structure interpretation, simultaneous amplitude variation with offset (AVO) inversion, 3D lithology prediction, and geologic integrated analysis. We present four key solutions to address four specific challenges in this case study: (1) the application of adaptive deghosting techniques to remove the source and streamer depth-related ghost notches in the seismic data bandwidth and the relative amplitude-preserved bandwidth extension technique to improve the seismic data resolution; (2) a practical rock-physics modeling approach to consider the formation overpressure for pseudoshear sonic log prediction; (3) interactive and synchronized workflow between prestack 3D AVO inversion and seismic processing to predict a 9 m thick layer in FM WC through more than 60 rounds of cyclic tests; and (4) cross validation between seismic qualitative attributes and quantitative inversion results to verify the lithology prediction result under the condition of insufficient well data.

Modeling processed seismic data to improve seismic facies prediction

2015 ◽  
Vol 3 (4) ◽  
pp. SAC91-SAC98 ◽  
Author(s):  
Adrian Pelham
Keyword(s):  
Seismic Data ◽  
Confusion Matrix ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Facies ◽  
Reservoir Modeling ◽  
Elastic Parameters ◽  
Frequency Model ◽  
Lithology Prediction ◽  
Well Data

Interpreters need to screen and select the most geologically robust inversion products from increasingly larger data volumes, particularly in the absence of significant well control. Seismic processing and inversion routines are devised to provide reliable elastic parameters ([Formula: see text] and [Formula: see text]) from which the interpreter can predict the fluid and lithology properties. Seismic data modeling, for example, the Shuey approximations and the convolution inversion models, greatly assist in the parameterization of the processing flows within acceptable uncertainty limits and in establishing a measure of the reliability of the processing. Joint impedance facies inversion (Ji-Fi®) is a new inversion methodology that jointly inverts for acoustic impedance and seismic facies. Seismic facies are separately defined in elastic space ([Formula: see text] and [Formula: see text]), and a dedicated low-frequency model per facies is used. Because Ji-Fi does not need well data from within the area to define the facies or depth trends, wells from outside the area or theoretical constraints may be used. More accurate analyses of the reliability of the inversion products are a key advance because the results of the Ji-Fi lithology prediction may then be quantitatively and independently assessed at well locations. We used a novel visual representation of a confusion matrix to quantitatively assess the sensitivity and uncertainty in the results when compared with facies predicted from the depth trends and well-elastic parameters and the well-log lithologies observed. Thus, using simple models and the Ji-Fi inversion technique, we had an improved, quantified understanding of our data, the processes that had been applied, the parameterization, and the inversion results. Rock physics could further transform the elastic properties to more reservoir-focused parameters: volume of shale and porosity, volumes of facies, reservoir property uncertainties — all information required for interpretation and reservoir modeling.

Shell's integrated quantitative interpretation workflow

2011 ◽  
Vol 51 (2) ◽  
pp. 681
Author(s):  
Frank Glass ◽  
Stephan Gelinsky ◽  
Irene Espejo ◽  
Teresa Santana ◽  
Gareth Yardley
Keyword(s):  
Seismic Data ◽  
Rock Properties ◽  
Well Log ◽  
Reservoir Properties ◽  
North West ◽  
The North ◽  
Fluid Properties ◽  
Well Data ◽  
Acoustic Responses ◽  
Carnarvon Basin

Shell Development Australia is a major asset holder in the Browse Basin and the Carnarvon Basin in the North West Shelf of Australia. In 2007, Shell Development Australia embarked on an integrated quantitative seismic interpretation project related to the Triassic Mungaroo Formation in the Carnarvon Basin. The main objective was to constrain the uncertainties in using seismic data as a predictor for rock and fluid properties of fields and prospects in the basin. This project followed a workflow that has been proven in other basins around the world, whereby the vertical and lateral variability of rock properties of both reservoir and non-reservoir lithologies are captured in general trends. The calculated trends are based on well log extractions of end member lithologies and the input of petrographic information and forward modelling. In combination with a regionally consistent 3D burial model for the estimation of remaining porosity, these established rock trends then allow for a prediction of various acoustic responses of reservoir and pore fill properties. The comparisons between the pre-drill predicted rock properties and the properties encountered after drilling at different reservoir levels have lead to a general confidence that the reservoir properties can be derived from seismic data where well data are not abundant. This increased confidence will play a major part in Shell’s attitude towards appraisal activities and decisions on various development options.

A successful qualitative and quantitative integrated interpretation of Lower Miocene wells and seismic data in Salina del Istmo Basin, Mexico

2021 ◽  
Vol 40 (12) ◽  
pp. 897-904
Author(s):  
Manuel González-Quijano ◽  
Gregor Baechle ◽  
Miguel Yanez ◽  
Freddy Obregon ◽  
Carmen Vito ◽  
...  
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Reservoir Rock ◽  
Amplitude Variation With Offset ◽  
Volcanic Material ◽  
Qualitative And Quantitative ◽  
Rock Types ◽  
Deep Waters ◽  
Well Data ◽  
Gulf Of Mexico Basin

The study area is located in middepth to deep waters of the Salina del Istmo Basin where Repsol operates Block 29. The objective of this work is to integrate qualitative and quantitative interpretations of rock and seismic data to predict lithology and fluid of the Early Miocene prospects. The seismic expression of those prospects differs from age-equivalent well-studied analog fields in the U.S. Gulf of Mexico Basin due to the mineralogically complex composition of abundant extrusive volcanic material. Offset well data (i.e., core, logs, and cuttings) were used to discriminate lithology types and to quantify mineralogy. This analysis served as input for developing a new rock-physics framework and performing amplitude variation with offset (AVO) modeling. The results indicate that the combination of intercept and gradient makes it possible to discriminate hydrocarbon-filled (AVO class II and III) versus nonhydrocarbon-filled rocks (AVO class 0 and IV). Different lithologies within hydrocarbon-bearing reservoirs cannot be discriminated as the gradient remains negative for all rock types. However, AVO analysis allows discrimination of three different reservoir rock types in water-bearing cases (AVO class 0, I, and IV). These conclusions were obtained during studies conducted in 2018–2019 and were used in prospect evaluation to select drilling locations leading to two wildcat discoveries, the Polok and Chinwol prospects, drilled in Block 29 in 2020.

Building tilted transversely isotropic depth models using localized anisotropic tomography with well information

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. D27-D36 ◽  
Author(s):  
Andrey Bakulin ◽  
Marta Woodward ◽  
Dave Nichols ◽  
Konstantin Osypov ◽  
Olga Zdraveva
Keyword(s):  
Seismic Data ◽  
Model Building ◽  
Symmetry Axis ◽  
Transverse Isotropy ◽  
Synthetic Data ◽  
Rock Physics ◽  
Transversely Isotropic ◽  
Data Set ◽  
Anisotropic Models ◽  
Well Data

Tilted transverse isotropy (TTI) is increasingly recognized as a more geologically plausible description of anisotropy in sedimentary formations than vertical transverse isotropy (VTI). Although model-building approaches for VTI media are well understood, similar approaches for TTI media are in their infancy, even when the symmetry-axis direction is assumed known. We describe a tomographic approach that builds localized anisotropic models by jointly inverting surface-seismic and well data. We present a synthetic data example of anisotropic tomography applied to a layered TTI model with a symmetry-axis tilt of 45 degrees. We demonstrate three scenarios for constraining the solution. In the first scenario, velocity along the symmetry axis is known and tomography inverts for Thomsen’s [Formula: see text] and [Formula: see text] parame-ters. In the second scenario, tomography inverts for [Formula: see text], [Formula: see text], and velocity, using surface-seismic data and vertical check-shot traveltimes. In contrast to the VTI case, both these inversions are nonunique. To combat nonuniqueness, in the third scenario, we supplement check-shot and seismic data with the [Formula: see text] profile from an offset well. This allows recovery of the correct profiles for velocity along the symmetry axis and [Formula: see text]. We conclude that TTI is more ambiguous than VTI for model building. Additional well data or rock-physics assumptions may be required to constrain the tomography and arrive at geologically plausible TTI models. Furthermore, we demonstrate that VTI models with atypical Thomsen parameters can also fit the same joint seismic and check-shot data set. In this case, although imaging with VTI models can focus the TTI data and match vertical event depths, it leads to substantial lateral mispositioning of the reflections.

AVO attribute inversion for finely layered reservoirs

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. C25-C36 ◽  
Author(s):  
Alexey Stovas ◽  
Martin Landrø ◽  
Per Avseth
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Oil Saturation ◽  
Seismic Properties ◽  
Seismic Input ◽  
Fluid Properties ◽  
Random Variability ◽  
Net To Gross ◽  
Avo Attributes

Assuming that a turbidite reservoir can be approximated by a stack of thin shale-sand layers, we use standard amplitude variaiton with offset (AVO) attributes to estimate net-to-gross (N/G) and oil saturation. Necessary input is Gassmann rock-physics properties for sand and shale, as well as the fluid properties for hydrocarbons. Required seismic input is AVO intercept and gradient. The method is based upon thin-layer reflectivity modeling. It is shown that random variability in thickness and seismic properties of the thin sand and shale layers does not change significantly the AVO attributes at the top and base of the turbidite-reservoir sequence. The method is tested on seismic data from offshore Brazil. The results show reasonable agreement between estimated and observed N/G and oil saturation. The methodology can be developed further for estimating changes in pay thickness from time-lapse seismic data.

A New, Data-Driven Methodology for the Integration of Seismic and Well Data Into Geological Model - Case Study Block 4, Offshore Angola, Lower Congo Basin

2020 ◽  
Author(s):  
Mario de Sá ◽  
Lidia. J Ferreira ◽  
Manuel. N José ◽  
Joào Simào ◽  
Wasiu Adedayo Sonibare ◽  
...  
Keyword(s):  
Congo Basin ◽  
Data Driven ◽  
Geological Model ◽  
Model Case ◽  
Well Data

Consistent Downscaling of Seismic Inversion Thicknesses to Cornerpoint Flow Models

SPE Journal ◽  
2008 ◽  
Vol 13 (04) ◽  
pp. 412-422
Author(s):  
Subhash Kalla ◽  
Christopher D. White ◽  
James Gunning ◽  
Michael Glinsky
Keyword(s):  
Covariance Matrix ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Flow Simulation ◽  
Rock Physics ◽  
Bayesian Framework ◽  
Seismic Inversion ◽  
Total Thickness ◽  
Flow Models ◽  
Well Data

Summary Accurate reservoir simulation requires data-rich geomodels. In this paper, geomodels integrate stochastic seismic inversion results (for means and variances of packages of meter-scale beds), geologic modeling (for a framework and priors), rock physics (to relate seismic to flow properties), and geostatistics (for spatially correlated variability). These elements are combined in a Bayesian framework. The proposed workflow produces models with plausible bedding geometries, where each geomodel agrees with seismic data to the level consistent with the signal-to-noise ratio of the inversion. An ensemble of subseismic models estimates the means and variances of properties throughout the flow simulation grid. Grid geometries with possible pinchouts can be simulated using auxiliary variables in a Markov chain Monte Carlo (MCMC) method. Efficient implementations of this method require a posterior covariance matrix for layer thicknesses. Under assumptions that are not too restrictive, the inverse of the posterior covariance matrix can be approximated as a Toeplitz matrix, which makes the MCMC calculations efficient. The proposed method is examined using two-layer examples. Then, convergence is demonstrated for a synthetic 3D, 10,000 trace, 10 layer cornerpoint model. Performance is acceptable. The Bayesian framework introduces plausible subseismic features into flow models, whilst avoiding overconstraining to seismic data, well data, or the conceptual geologic model. The methods outlined in this paper for honoring probabilistic constraints on total thickness are general, and need not be confined to thickness data obtained from seismic inversion: Any spatially dense estimates of total thickness and its variance can be used, or the truncated geostatistical model could be used without any dense constraints. Introduction Reservoir simulation models are constructed from sparse well data and dense seismic data, using geologic concepts to constrain stratigraphy and property variations. Reservoir models should integrate spare, precise well data and dense, imprecise seismic data. Because of the sparseness of well data, stochastically inverted seismic data can improve estimates of reservoir geometry and average properties. Although seismic data are densely distributed compared to well data, they are uninformative about meter-scale features. Beds thinner than about 1/8 to 1/4 the dominant seismic wavelength cannot be resolved in seismic surveys (Dobrin and Savit 1988; Widess 1973). For depths of ˜3000 m, the maximum frequency in the signal is typically about 40 Hz, and for average velocities of ˜2,000 m/s, this translates to best resolutions of about 10 m. Besides the limited resolution, seismic-derived depths and thicknesses are uncertain because of noise in the seismic data and uncertainty in the rock physics models (Gunning and Glinsky 2004, 2006). This resolution limit and uncertainties associated with seismic depth and thickness estimates have commonly limited the use of seismic data to either inferring the external geometry or guiding modeling of plausible stratigraphic architectures of reservoirs (Deutsch et al. 1996). In contrast, well data reveal fine-scale features but cannot specify interwell geometry. To build a consistent model, conceptual stacking and facies models must be constrained by well and seismic data. The resulting geomodels must be gridded for flow simulation using methods that describe stratal architecture flexibly and efficiently.

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