The aim of this study is to propose a stratigraphic and sedimentary framework though the integration of available sedimentary, diagenetic and petrophysical data, which will be utilized in the construction of a high resolution stratigraphic framework, as an input into comprehensive review and update of an existing model of heterogeneous carbonate reservoir in a mature field in Abu Dhabi, UAE.
Depositional facies have been defined in cored wells, subsequently were associated taking into account the biologic and sedimentary processes in response of carbonate growing and sea level changes, allowing the identification of the main stratigraphic surfaces.
Surfaces can extend the correlation along the field and define the model of facies that, with the evidence provided by cores, can recreate and predict the different regressive-transgressive cycles in high resolution which the carbonate platform were undergone during its evolution.
Diagenetic evolution, interpreted through laboratory observations, was integrated with facies and petrophysical evaluation allowing the understanding of the spatial distribution of petrophysical properties within a heterogeneous reservoir and define a new set of facies which will be used in the generation of geological static model.
Application of sequence stratigraphy methods in cores, and extended in logs allowed the identification of six depositional sequences, with thicknesses of 2 to 4 meters each, corresponding to the phases of carbonate platform growth. Within each depositional sequences, typical cycles were defined that support the understanding in the association of facies and their relationship during the deposition.
The identification of sedimentological cycles not only genetically organizes the facies and predicts the stacking pattern, but also makes possible to find an excellent correspondence between cycles from lowstand system track intervals with good to excellent permeability values, and cycles from transgressive system track intervals with low permeabilities.
Many of the sequence stratigraphy published articles driven for the most important reservoirs along the Arabian Plate, provide an excellent tool in the regional correlation. However, they are not enough to be used in the reservoir characterization in detail that is required during the development of the field neither as input data in the generation of geological static models that use the sedimentary trends as constrain to populate the petrophysical properties.
The process of depositing particulate organic carbon in the rock record is controlled by three main factors which impact organic enrichment through rates of change at the time of deposition of: primary organic production, organic decomposition, and dilution from non-organic (mineral) constituents and biogenic silica or carbonate. Sedimentation rate, which is commonly the least constrained variable, impacts organic matter through: (1) the volume of deposition of non-organic relative to organic constituents and (2) the duration of time that organic matter is exposed to surficial processes whereby organic matter can be lost by oxidation or predation. Depositional processes (tractional vs. turbid vs. debritic vs. hemipelagic) are also controls on the bounding limits of organic-richness of sediments.
Here we examine a core taken through the Sakmarian to Artinskian (Wolfcampian/Leonardian) Wolfcamp B, A (operational stratigraphic divisions) and Dean Formations of the Midland Basin in Reagan County, Texas. This well is in a basinal position ~40km southwest of the paleo-shelf edge at the time of deposition. At the top and base of the studied interval both palynofloral and microfaunal data have been tied to regional events for which absolute ages have been determined and there are two unconformable surfaces for which we have estimated likely durations. Intervals of higher sedimentation correlate with intervals of higher TOC than those with lower sedimentation rates. Other studies in the Midland Basin (and on this core) show relatively low Molybdenum (a proxy for bottom water oxygenation) concentrations which has been interpreted as very low water turn-over rates and restricted conditions in the Midland Basin during Wolfcamp deposition. We hypothesize it is unlikely that organic matter enrichment was promoted by restricted water circulation alone and that high sedimentation rates equate with the rapid burial of organic matter that reduced the duration of exposure to decay at the seabed. Sedimentation is interpreted to have occurred primarily through sediment gravity driven turbidity and hybrid flow processes which limited the degree of hydraulic fractionation of organic matter and thus sediment dilution that could occur. The controls of sedimentation and organic productivity will be examined through climate and source-rock prediction models, a hypothesis for reservoir distribution and an approach towards upscaling these results will also be discussed.
The classic tripartite parasequence set scheme is widely used in coastal plain sequence stratigraphic interpretations which respond to a relative sea level cycle. The Highstand System Tract (HST) is immediately followed by a Lowstand Systems Tract (LST). The ensuing Transgressive System Tract (TST) accompanies the flooding stage, but there is no corresponding parasequence set during a fall in relative sea level fall. In theory a Regressive Systems Tract (RST), would be produced during a relative seal level fall, where it would erode and replace portions of the upper HST and would lie beneath in time (though not in the stratal record) the ensuing LST and would be characterized by offlap and basinward shift in facies (e.g. the FSST distal clastic wedges descrbed by Plint and Nummedal, 2000). However, as preserved deposition is minimal during the fall, an RST would be less well-preserved in a deltaic setting. The purpose of this study is to demonstrate that the RST which responds to a drop in base level indeed can be found in ramp coastal plain settings, where they are represented as fluvial terraces. The example comes from offshore Thailand.
The neglect of the RST in stratigraphic interpretation can be attributed in part to the initial evolution of the systems tract concept and subsequent confusion of this concept in different theories concerning the effects of base level changes upon depositional sequences (Pigott et al., 2011).
First, owing to the difficulty of recognition of offlapping strata in early 2D seismic sections, the subdivision of sequences into the classic component systems tracts was first presented by Posamentier and Vail (1988) where a highstand was immediately followed by lowstand. The limited recognition of sediments deposited during relative sea-level fall led to the early representation of relative sea-level fall as 'instantaneous' and the asymmetry of the relative sea-level curve was later attributed to incomplete preservation of the sedimentary record with the classical definition of prograding, aggrading, and retrogradational system tracts which were then defined as the parasequence sets termed HST, LST, and TST (van Wagoner et al., 1988).
Second, in consideration of a complete sea level cycle, as several previous workers have addressed the sea-level falling stage in different theories (Catuneanu, 2007), there exists a general lack of a unanimous agreement on the division of base level falling from onset to the end. There are four opinions: (1) early LST fan (Haq, 1987; Posamentier, 1988; and Galloway, 1989; etc.); (2) Late HST (vanWagoner 1995, etc.); (3) FSST (Hunt and Tuck, 1995, etc.); (4) RST (Embry, 1993, etc.).
Lithologic reservoirs, characterized by the subtle location, small dimension and complex depositional architecture, have attracted much attention as a focus issue in petroleum exploration and development. Conventional structure and deposition interpretations based on seismic and logging data are very beneficial to the distribution forecast and formation analysis of lithologic reservoirs. But they are not enough to guide the remaining oil prediction of these reservoirs in the late stage of oil and gas development. Through years of study, a new fine characterization method on lithologic reservoirs is proposed. And four key techniques are involved, including the establishment of the depositional isochronous stratigraphic framework, dynamic analysis of the reservoir depositional evolution, fine anatomization of the reservoir architecture and comprehensive prediction of the remaining oil. This method has been successfully applied to a complex lithologic reservoir located at the XJ 24-1 Oilfield in the northern Pearl River Mouth Basin and verified by the later drilling results.
High-quality seismic imaging results are the basis of lithologic reservoir characterization. Since the NMO and post-stack time migration were proposed respectively by Dix (1955) and Claerbout (1972), seismic imaging theory has been developed from NMO + DMO + post-stack migration, pre-stack time migration (PSTM), pre-stack depth migration (PSDM), anisotropic medium PSDM to the orthogonal crystal medium PSDM. The lateral resolution of seismic data is gradually improved.
Sequence stratigraphy and Seismic sedimentology are the major theories to study the stratigraphic framework and depositional evolution in a depression or an oilfield. Vail (1987) proposed the concept of sequence stratigraphy based on a passive continental margin sedimentary model, and established sequence stratigraphy theory that is based on seismic, logging and geological outcrop data. Furthermore, this theory was improved constantly and applied to hydrocarbon exploration of almost all kinds of basins (Galloway, 1989; Posamentier et al., 1992; Embry, 1993). Seismic sedimentology is the use of seismic data to study sedimentary rocks and their forming processes (Zeng et al., 1998). It is a new marginal interdisciplinary subject following sequence stratigraphy (Schlager, 2000) and bring new understandings to the limitation of the seismic resolution in space (Ling et al., 2007). Seismic interpretation based on sequence stratigraphy and seismic sedimentology could reduce the exploration risk of lithologic reservoirs. However, it is difficult to study the architecture and heterogeneity of lithologic reservoirs.
The Eastern Barents, Kara, Laptev, East Siberian seas and the western Chukchi Sea occupy a large part of the Eurasian shelf in the Russian Arctic. This huge region consists over 40 sedimentary basins of variable age and genesis, which are thought to bear significant undiscovered hydrocarbon resources. Apart of the East Barents and South Kara shelves with proven world-class gas and gas condensate resources, the rest of the basins are undrilled and explored with rather sparse grid of regional 2D seismic lines.
The majority of the sedimentary basins were formed and developed in a rift and post-rift setting and later modified through a series of structural inversions. Important tectonic events controlling the structure and petroleum geology of the basins are: the Caledonian collision and orogeny followed by Late Devonian to Early Carboniferous rifting, Late Paleozoic Baltica/Siberia collision and Uralian orogeny, Triassic and Early Jurassic rifting, Late Jurassic to Early Cretaceous Canada Basin opening accompanied by closure of the Anyui Ocean, the Late Mesozoic Verkhoyansk-Brookian orogeny, and Cenozoic opening of the Eurasia Oceanic Basin.
Using available regional seismic lines correlated with borehole data, onshore geology in areas with no exploration drilling, and the Arctic-wide magnetic, bathymetry and gravity grids, we provide more confident characterization of the regional structural elements of the Russian Arctic shelf, and constrain the timing of basin formation, structural styles, lithostratigraphy and possible petroleum systems and plays in frontier areas.
The last decade offshore oil and gas has become the main source of growth of the world's hydrocarbon reserves. Special interest to the geological structure of the Russian Arctic Seas comes from very high hydrocarbon potential of their sedimentary basins, which are expected to take a leading role in future world power supply.
Geological and geophysical characteristics of the Russian Arctic Sea sedimentary basins allowed to estimate their hydrocarbon potential by comparison with the known world analogues. Due to difficult climatic conditions in Arctic, we consider only the potential resources of giant (more than 500 million barrels of oil equivalent) deposits. With a limited seismic data, data of gravity and magnetic surveys allowed to outline the main regional structures, to ascertain relations of the discovered hydrocarbon fields to the identified structures. Five major gas-condensate and gas fields are still discovered in this region: three (Shtokman, Ludlov, Ledovoe) in the Barents and two (Leningrad and Rusanov) in the Kara Sea.
Primarily structure of sedimentary basins of the Russian Arctic Seas was investigated by geophysical methods. One of successful methods of integrated interpretation of geophysical data is a simulation. Simulation is performed based on computer calculations of gravity effects producing by density heterogeneity of lower hemisphere. The following density models of the Earth crust used seismic and geological data related mainly to the basin sedimentary cover, as well as a few data on the deep seismic boundaries (Piskarev, 2004). Sites of direct geological observations relate mostly to folded belts and basement rocks in the periphery areas.
The structure of sedimentary basins of the Russian Arctic Seas is illustrated by a number of density models which are prepared by iterative computation method. Location of sections on which the simulation was performed and which are described below, is shown in Fig. 1.
This paper describes the procedure of building a probabilistic decision tree on the basis of the integration of data from multiple sources, conditional probabilities, and the application to map fracture corridors (FCs) in a mature oil field with abundant production data. A fracture corridor is a tabular, subvertical, fault-related fracture swarm that intersects the entire reservoir and extends laterally for several tens or hundreds of meters. Direct indicators of fracture corridors, such as image logs, flow profiles, well tests, and seismic fault maps, are sometimes insufficient to map all fracture corridors in a field. It is also necessary to use indirect fracture-corridor indicators from well data, such as productivity index (PI), gross rate, water cut, and openhole logs. Fracture corridors from indirect indicators can be inferred by a probabilistic decision tree, which makes predictions by integrating data from multiple sources, giving preference to the indicators with the highest relevance. Decision trees are constructed by use of a training set that includes measurements of both direct and indirect fracture-corridor indicators. In this study, wells with borehole images, production logs (flow profiles), and injector/producer short cuts are selected as the training set. The resulting decision trees reveal that total losses, gross production rates, and water cuts are the three most effective indirect indicators of fracture corridors in the test field.
It is often the case that a particular reservoir attribute, such as porosity, has only sparse direct measurements. It is possible, however, to predict values of such a target variable with the help of a set of other variables that exhibit some degree of correlation to the target variable and have abundant measurements. A common example is estimating porosity from seismic attributes. In this paper, the variables that have one-to-one correspondence to the target variable are called direct indicators and the variables that have some degree of correlation are called indirect variables. For example, density and neutron logs are direct indicators of porosity, whereas seismic impedance is an indirect indicator.
There are several statistical techniques to predict a target variable from a set of indirect indicators, and these can be collected under two main groups: supervised prediction techniques and unsupervised prediction techniques.
In the case of supervised prediction techniques, indirect indicators are correlated to a target variable by use of a training set of data that includes measurement of both direct and indirect indicators of the target variable. The generated predictive system can be used to estimate values of the target variable solely on the basis of indirect indicators in wells that do not have any measurement of direct indicators. Multiple regression, back propagation, neural networks, and Bayesian decision trees belong to this category.
In cases where the training set is small or no direct indicators are available, it is possible to adopt statistical techniques that do not require extrapolation from a training set. These are termed unsupervised prediction techniques. Several such techniques exist, including cluster analysis, unsupervised neural networks, and factor analysis (Wasserman 1989; Chester 1993; Van De Geer 1971). The basic idea is to discover hidden factors that control indicator variables and to interpret these factors in terms of the target variable. For example, the density (spacing/relative abundance) of conductive fractures may affect the rapid water-cut rise, high initial PI, and high gross rate. These three indirect indicators will be highly correlated to each other. An unsupervised prediction technique may uncover the hidden factor (fracture density) that controls all three variables from the high correlation among them.
Both supervised and unsupervised inferences are methods for making predictions with incomplete information (Tamhane et al. 2000; Fletcher and Davis 2002). Most of the applications in the oil industry use fuzzy logic or fuzzy neural networks. These applications also use soft computing decision making with incomplete evidence and risk reduction by use of a fuzzy-expert system (Weiss et al. 2001; Chen et al. 2002; Saggaf and Nebrija 2003). This idea has found some application, especially in mapping fracture density by use of seismic attributes (Ouenes et al. 1995; Zellou et al. 2003; Bloch et al. 2003).
Both supervised and unsupervised statistical techniques aim at determining some global attribute of dispersed fractures, such as density. It is often fracture corridors, however, rather than dispersed fractures that are characterized as the main reservoir heterogeneity (Ozkaya and Richard 2006). An FC is a tabular, subvertical, fault-related fracture swarm that intersects the entire reservoir and extends laterally for several tens or hundreds of meters (Fig. 1). FCs could be fluid-conductive or cemented. In this paper, an FC denotes a fluid-conductive FC unless otherwise specified. FCs may have significant conductivity and may play a major role in reservoir dynamics by providing pressure support and, therefore, causing early water breakthroughs and increased gross rates.
The four main requirements to map an FC are location, strike, length, and conductivity. Here, we focus primarily on locating FCs and discuss only briefly how other attributes can be estimated. Our objective is not the actual mapping of FCs but examining Bayesian decision trees as a viable technique in FC identification. The basis and procedures for calculating conditional probabilities, entropy, information Gain (IG), and the construction of decision trees are explained in the Appendix.
Ozkaya, Sait Ismail (Shell) | Gordon, Stephen (Petroleum Development Oman) | McFarlane, Allan Robert (Petroleum Development Oman) | Siyabi, Salim (Petroleum Development Oman) | Al-Busaidi, Salam Mahmoud (Petroleum Development Oman) | Kramer, Frans Michael (Baker Atlas Geoscience) | Coskun, Sefer B. (Baker Atlas Geoscience) | Bolle, Luc
The subject of this paper is identification and mapping fracture corridors in a carbonate field in Oman using a probabilistic approach. A fracture corridor is a tabular sub-vertical fault-related fracture swarm which intersects the entire reservoir and extends laterally for several tens or hundreds of meters. The only direct indicator of fracture corridors are borehole image logs. Unlike openhole logs and other borehole measurements, image logs are available only from a limited number of wells. It is necessary to utilize indirect indicators to identify and map fracture corridors, such as lost circulation, step flow profiles, water breakthrough or seismic lineaments. This fits well into a Bayesian scheme to infer the cause from the manifestations. The conditional probability of having a fracture corridor given an indirect indicator such as mud loss is calculated from wells with image logs. Some indirect indicators have no direct link to image logs and a Bayesian inference has to be used to find the conditional probability through an intermediate indicator. For example, the probability of having a fracture corridor given step flow profile is calculated from the conditional probability of having mud losses given a step flow profile, and probability of having a fracture corridor given mud losses. Conditional probability graphs are constructed for continuous variables such as water cut, gross rates and injection rates. Threshold values are defined for these variables such that the probability of having a fracture corridor is more than 0.5 if the indicator is greater than the threshold value.
Good indicators of fracture corridors include mud losses, step flow profiles and water fingering in horizontal wells. High gross rates and water cut are also good indicators but injection rates and sweep in vertical well are not very reliable. The predictive power of indirect indicators improves drastically if two or more indicators favour a fracture corridor. If the wells with fracture corridor indicators are aligned in WNW or NW, the dominant fracture corridor directions in this particular field, the likelihood of having a fracture corridor increases. Final result shows most fracture corridors are located on the southern and northern flanks of the field with a relatively low degree of fracturing in creastal area.
Orientation of fracture corridors from indirect indicators must be estimated from nearby image fracture corridors or seismic faults or from dominant fracture strike in different sectors of the field. The dominant fracture orientation is WNW with one NW fault/ fracture zone on the western flank of this Field. There are also a few NE fracture corridors from short cuts, image logs and well alignment.
An inferred corridor is extended in selected direction (WNW or NW) until it reaches the exclusion zone. Exlusion zones include circle of investigation of well tests with radial flow, horizontal wells with no fracture corridor or a circle around typical matrix producers or injectors. The radius of the circle is determined from the average zone of influence of fracture fairways, which is about 50 m.
The total length of fracture corridors is estimated from image log scan line density. The total number of fracture corridors is estimated from the total length and average length of fracture corridors. The difference between the total number and the number of fracture corridors captured by data analysis is the number of fracture corridors that escaped detection. This number is used to infill stochastic fracture corridors and to generate fracture corridor density and permeability maps.