Lithofacies Model Research Articles

Organic-rich Marcellus Shale lithofacies modeling and distribution pattern analysis in the Appalachian Basin

The Marcellus Shale is considered to be the largest unconventional shale-gas resource in the United States. Two critical factors for unconventional shale reservoirs are the response of a unit to hydraulic fracture stimulation and gas content. The fracture attributes reflect the geomechanical properties of the rocks, which are partly related to rock mineralogy. The natural gas content of a shale reservoir rock is strongly linked to organic matter content, measured by total organic carbon (TOC). A mudstone lithofacies is a vertically and laterally continuous zone with similar mineral composition, rock geomechanical properties, and TOC content. Core, log, and seismic data were used to build a three-dimensional (3-D) mudrock lithofacies model from core to wells and, finally, to regional scale. An artificial neural network was used for lithofacies prediction. Eight petrophysical parameters derived from conventional logs were determined as critical inputs. Advanced logs, such as pulsed neutron spectroscopy, with log-determined mineral composition and TOC data were used to improve and confirm the quantitative relationship between conventional logs and lithofacies. Sequential indicator simulation performed well for 3-D modeling of Marcellus Shale lithofacies. The interplay of dilution by terrigenous detritus, organic matter productivity, and organic matter preservation and decomposition affected the distribution of Marcellus Shale lithofacies distribution, which may be attributed to water depth and the distance to shoreline. The trend of normalized average gas production rate from horizontal wells supported our approach to modeling Marcellus Shale lithofacies. The proposed 3-D modeling approach may be helpful for optimizing the design of horizontal well trajectories and hydraulic fracture stimulation strategies.

A multi-objective stochastic optimization approach for estimation of subsurface geomodels

We present a multi-objective optimization approach to the subsurface geomodel updating problem using stochastic search techniques. This is a new approach to the geomodeling process for which a variety of direct and indirect measurements can be simultaneously fit to the geomeodel. Due to the inherent uncertainties and noise in real data measurements, geological and geophysical datasets acquired in the same area may be in conflict with each other; and a realistic subsurface model can only be obtained by simultaneously integrating the combined datasets in a reasonable manner. One approach to this problem is to perform joint inversion of multiple geological and/or geophysical datasets, where an optimal model is achieved by optimization of a linear combination of several objective functions measuring the match of the simulated datasets with the observed datasets. In this paper, we consider joint inversion of multiple datasets for geomodel updating, as a Multi-Objective Optimization Problem (MOOP), where separate objective functions for each subset of the observed data are defined. Then, a stochastic optimization technique is employed to find the set of best-compromise model solutions that fit the defined objectives, which are found along the Pareto front (a set of non-dominated optimal solutions). We demonstrate that a customized initialization of the algorithm can speed up the convergence and result in a set of improved model solutions. An application of the methodology is presented using the example of a 3D reservoir litho-facies model that must honour a set of geological and geophysical attributes (e.g. log data and inverted seismic P- and S-wave impedances). Also, the sensitivity of the model inversion results to changes in the optimization parameters and the presence of seismic noise is analysed.

Optimization of the Markov chain for lithofacies modeling: an Iranian oil field

Reconnaissance and interpretation of underground heterogeneity, particularly lithofacies, always plays an important role in evaluation and management of hydrocarbon resources. Between various methods presented for modeling discrete characteristics of hydrocarbon reservoirs such as lithofacies, one with a more proper conformity with actual condition of reservoir facies is of great advantage. Formed on the basis of probability and presenting transition matrix, the Markov method is widely applied as a powerful tool for modeling the facies. In the present study, first, the method is introduced in details; then, in order to optimize it for conditions with insufficient well data, two suggestions are made based on changing the motion direction of the chain and increasing the conditional boundary in simulation procedure. The case study is a 12-km-long 110-m-thick section of anhydrite and three major members of the Asmari Formation from an oil field in southwest Iran. This section is modeled through Markov classical procedure, changing chain motion direction and finally adding one seismic horizon as another conditional boundary. The models set indicated that on the basis of using the data from three wells and seven seismic horizons, the best result with 86 % accuracy is for the state of using two conditional boundaries.

Lithofacies prediction from well log data using a multilayer perceptron (MLP) and Kohonen's self-organizing map (SOM) – a case study from the Algerian Sahara

In this paper, a combination of supervised and unsupervised leanings is used for lithofacies classi- fication from well log data. The main idea consists of enhancing the multilayer perceptron (MLP) learning by the output of the self-organizing map (SOM) neural network. Application to real data of two wells located the Algerian Sahara clearly shows that the lithofacies model built by the neural combination is able to give better results than a self-organizing map.

The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction

The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification.

Evaluation of oil sands resources —A case study in the Athabasca Oil Sands, NE Alberta, Canada

Oil sands are the most important of the oil and gas resources in Canada. So the distribution and evaluation of oil sands form a critical basis for risk investment in Canada. Distribution of oil sands resources is severely controlled by the reservoir heterogeneity. Deterministic modeling is commonly used to solve the heterogeneity problems in the reservoir, but rarely used to evaluate hydrocarbon resources. In this paper, a lithofacies based deterministic method is employed to assess the oil sands resources for a part of a mining project in northern Alberta. The statistical analysis of Dean Stark water and oil saturation data and study of the core description data, regional geology and geophysical logs reveal that the lithofacies in the study area can be classified into reservoir fades, possible reservoir fades and non-reservoir fades. The indicator krigging method is used to build a 3D lithofacies model based on the classification of sedimentary fades and the ordinary krigging method is applied to petrophysical property modeling. The results show that the krigging estimation is one of the good choices in oil sand resources modeling in Alberta. Lithofacies-grade based modeling may have advantages over the grade-only based modeling.

High-resolution facies and porosity models of the upper Jurassic Arab-D carbonate reservoir using an outcrop analogue, central Saudi Arabia

Subsurface models of hydrocarbon reservoirs are coarse and of low resolution when compared with the actual geologic characteristics. Therefore, the understanding of the three-dimensional architecture of reservoir units is often incomplete. Outcrop analogues are commonly used to understand the spatial continuity of reservoir units. In this study, a Late Jurassic outcrop analogue for the Arab-D reservoir of central Saudi Arabia was used to build a high-resolution model that captures fine geologic details. Subsurface reservoir lithofacies were matched with those from the studied outcrop, and porosity values derived from published core and well log data from the Ain Dar, Uthmanyah, and Shudgum areas of the Ghawar Field, eastern Saudi Arabia, were then applied to the equivalent lithofacies in the outcrop. Maximum, minimum, and average subsurface porosity for each lithofacies were distributed in the facies model using a geostatistical algorithm to produce nine porosity models for the field data. Several realisations were run to visualise the variability in each model and to quantitatively measure the uncertainty associated with the models. The results indicated that potential reservoir zones were associated with grainstone, packstone, and some wackestone layers. Semivariogram analysis of the lithofacies showed good continuity in the N-S direction and less continuity in the E-W direction. The high-resolution lithofacies models detected permeability barriers and isolated low porosity bodies within the potential reservoir zones. This model revealed the porosity distribution in areas smaller than one cell in the subsurface model and highlighted the uncertainty associated with several aspects of the model.

Marcellus Shale Lithofacies Prediction by Multiclass Neural Network Classification in the Appalachian Basin

Marcellus Shale is a rapidly emerging shale-gas play in the Appalachian basin. An important component for successful shale-gas reservoir characterization is to determine lithofacies that are amenable to hydraulic fracture stimulation and contain significant organic-matter and gas concentration. Instead of using petrographic information and sedimentary structures, Marcellus Shale lithofacies are defined based on mineral composition and organic-matter richness using core and advanced pulsed neutron spectroscopy (PNS) logs, and developed artificial neural network (ANN) models to predict shale lithofacies with conventional logs across the Appalachian basin. As a multiclass classification problem, we employed decomposition technology of one-versus-the-rest in a single ANN and pairwise comparison method in a modular approach. The single ANN classifier is more suitable when the available sample number in the training dataset is small, while the modular ANN classifier performs better for larger datasets. The effectiveness of six widely used learning algorithms in training ANN (four gradient-based methods and two intelligent algorithms) is compared with results indicating that scaled conjugate gradient algorithms performs best for both single ANN and modular ANN classifiers. In place of using principal component analysis and stepwise discriminant analysis to determine inputs, eight variables based on typical approaches to petrophysical analysis of the conventional logs in unconventional reservoirs are derived. In order to reduce misclassification between widely different lithofacies (for example organic siliceous shale and gray mudstone), the error efficiency matrix (ERRE) is introduced to ANN during training and classification stage. The predicted shale lithofacies provides an opportunity to build a three-dimensional shale lithofacies model in sedimentary basins using an abundance of conventional wireline logs. Combined with reservoir pressure, maturity and natural fracture system, the three-dimensional shale lithofacies model is helpful for designing strategies for horizontal drilling and hydraulic fracture stimulation.

Methodology of organic-rich shale lithofacies identification and prediction: A case study from Marcellus Shale in the Appalachian basin

The success of shale gas in North America has attracted increased interest in “unconventional” reservoirs. Two critical factors for shale-gas reservoirs are units amenable to hydrologic fracture stimulation and sufficient natural gas content. The effectiveness of hydrologic fracture stimulation is influenced by rock geomechanical properties, which are related to rock mineralogy. The natural gas content in shale reservoirs has a strong relationship with organic matter, which is measured by total organic carbon (TOC). A 3D shale lithofacies model constructed using mineral composition, rock geomechanical properties and TOC content can be applied to optimize the design of horizontal well trajectories and stimulation strategies. Core analysis data, log data and seismic data were used to build a 3D shale lithofacies from core to well and finally to regional scale. Core, advanced and common logs were utilized as inputs to petrophysical analysis, and various pattern recognition methods, such as discriminant analysis, fuzzy logic, neural network and support vector machine. A limited set of eight derived parameters from common logs were determined as critical inputs for pattern recognition methods. Advanced logs, such as pulsed neutron spectroscopy, are used to determine mineral composition and TOC data improve and confirm the quantitative relationship between conventional logs and lithofacies. Seismic data, interpreted sequence stratigraphy and depositional environments were used as constraints to build deterministic and stochastic 3D lithofacies models and to extrapolate lithofacies from well scale to regional scale.

3D modelling of the shallow subsurface of Zeeland, the Netherlands

AbstractThe Geological Survey of the Netherlands aims at building a 3D geological voxel model of the upper 30 m of the subsurface of the Netherlands in order to provide a sound basis for subsurface related questions on, amongst others, groundwater extraction and management, land subsidence studies, aggregate resources and infrastructural issues. The Province of Zeeland (SW Netherlands, covering an area of approximately 70 by 75 km) was chosen as the starting point for this model due to an excellent dataset of 23,000 stratigraphically interpreted borehole descriptions.The modelling procedure involved a number of steps. The first step is a geological schematisation of the borehole descriptions into units that have uniform sediment characteristics, using lithostratigraphical, lithofacies and lithological criteria. During the second modelling step, 2D bounding surfaces are constructed. These surfaces represent the top and base of the lithostratigraphical units and are used to place each voxel (100 by 100 by 0.5 metres) in the model within the correct lithostratigraphical unit. The lithological units in the borehole descriptions are used to perform a final 3D stochastic interpolation of lithofacies, lithology (clay, sand, peat) and if applicable, sand grain-size class within each lithostratigraphical unit. After this step, a three-dimensional geological model is obtained. The use of stochastic techniques such as Sequential Gaussian Simulation and Sequential Indicator Simulation, allowed us to compute probabilities for lithostratigraphy, lithofacies and lithology for each voxel, providing a measure of model uncertainty.The procedures described above resulted in the first fully 3D regional-scale lithofacies model of the shallow subsurface in the Netherlands. The model provides important new insights on spatial connectivity of sediment units of, for example, sandy Holocene tidal channel systems. Our results represent a major step forward towards a fully 3D voxel model of the Netherlands.

Investigation of lithofacies predictability using the Shannon (information) entropy theorem: the Upper Eocene “Górka Lubartowska” amber deposit of the Siemien Formation

A generalized workflow of scientific process requires data to be obtained, reprocessed, integrated, optionally transformed, modelled and finally interpreted in order to understand the underlying process. This procedure is affected by both objective and subjective uncertainties. In parallel with the development of geostatistics, the role of uncertainty has been widely investigated in geosciences. This has led to the introduction of new concepts, taken for example from thermodynamics, such as entropy. Predicting the subsurface is an especially thankless effort, as data are driven from spatially highly limited direct sources. The following paper provides an review of various applications of the Shannon entropy theorem in geoscience. Information entropy, initially proposed by Shannon (1948) provides an objective measure of overall system uncertainty. Significant concern has been focused on the application of Shannon entropy to provide an objective measure of joint system uncertainty and visualization of its spatial distribution. The area of extensively drilled Eocene amber-bearing deposits located in the Lubelskie voivodeship was selected as a case study to investigate the quality of prediction stochastic lithofacies models. The importance of adding secondary variables to a stochastic model is also reviewed here. Adding new data and rerunning the simulation allows assessment of its impact on the predictability of a stochastic model. The most important conclusion from the study is that the deposition of amber-bearing lithofacies occurred mostly in the northern part of the area investigated, as shown also by ongoing exploitation of the deposit.

A digital, spatial, geological model of the Miocene in Jylland, Denmark

A major hydrogeological programme has been carried out to map the Miocene succession in central and southern Jylland (Fig. 1). The Miocene deposits comprise several aquifers with potential drinking water resources and have been investigated by drilling and acquisition of seismic data integrated with sedimentology and biostratigraphy. Scharling et al. (2009) described a 3D hydrogeological model that covers part of the onshore Danish Miocene deposits. The model was based on a sequence-stratigraphic approach and led to a better understanding of the geological architecture of the aquifers than traditional lithofacies models. Hence it was decided to establish a digital, spatial, geological model covering the entire onshore Miocene succession (Kristensen et al. 2010).

A lithofacies model of upper jurassic productive deposits in the eastern part of the Shirotnoye Priobie region (Western Siberia)

On the basis of an integrated interpretation of well data, core analysis, geophysical well logging, and seismic data a new geological model of the JV11−2 stratum is suggested for one of the Shirotnoye Priobie areas in Western Siberia. The results of acoustic inverse and the rock elastic property research were used for the validation of lithofacies zonation. Supplementary exploration of the area and production drilling are recommended.

A lithofacies model for the Upper Devonian Pamyatno-Sasovskoye reef (oilfield) (Volgagradskoe Povolzhye, Russia)

Based on the exploratory drilling data provided by LLC LUKOIL-Nizhnevolzhskneft, we studied the structure, composition, and conditions of formation of the middle-upper Frasnian sections of the Pamyatno-Sasovskoye oilfield, Central Russia, which are related to a complex organogenic build-up. We also conducted a lithofacies section modeling based on the data derived from classification of secondary dolomitic limestones (as per Dunham's classification), palaeontological components (e.g., algae, stromatoporoids, crinoids, gastropods, protozoans), structural constructions, and logging data.

On the value of lithofacies data for improving groundwater flow model accuracy in a three‐dimensional laboratory‐scale synthetic aquifer

Improvement of the prediction accuracy of groundwater flow models has been receiving substantial attention from many researchers through the development of enhanced characterizations of the structure of subsurface lithofacies and of the distribution of hydraulic conductivity. In this study, we investigated how incorporating increasing amounts of lithofacies data into the construction of a conceptual model of aquifer heterogeneity helps to reduce prediction error and uncertainty in groundwater flow models. An approach based on both laboratory experiments and numerical simulations was tested using data from an intermediate‐scale synthetic heterogeneous aquifer. The heterogeneous aquifer consisted of five lithofacies, corresponding to five test sands. Three pumping tests were conducted and provided experimental data to perform groundwater flow model calibration and validation. The pumping tests were also simulated numerically in order to provide a series of error‐free synthetic hydraulic data sets. On the basis of Markov chains models of transition probabilities, a total of 901 random realizations of the heterogeneous distribution of lithofacies were created using varying amounts of conditioning lithofacies data sampled along randomly placed hypothetical boreholes. For each realization and for two other simplified lithofacies models, parameter estimation was performed to estimate the hydraulic conductivity of the lithofacies using the experimental and synthetic hydraulic data from the three pumping tests. The results generally showed that the use of more lithofacies data in the construction of the lithofacies realizations led to an improvement in groundwater flow model prediction accuracy. When using the error‐free synthetic hydraulic data, the calibration‐prediction error and uncertainty decreased drastically when the mean borehole spacing was on the order of twice the horizontal correlation length or less. When the experimental hydraulic data were used, this drastic improvement in the calibration‐prediction error was somewhat obscured and, in some cases, exhibited a local minimum. This local minimum, although beyond practical limits, corresponded to an optimal number of boreholes. Finally, the effect of incorporating more lithofacies data for the construction of lithofacies realizations was found to have a similar impact on the quality of model calibration and on the quality of predictive simulations conducted using the calibrated model.

History matching of approximated lithofacies models under uncertainty

In history matching of lithofacies reservoir model, we attempt to find multiple realizations of lithofacies configuration that are conditional to dynamic data and representative of the model uncertainty space. This problem can be formalized in the Bayesian framework. Given a truncated Gaussian model as a prior and the dynamic data with its associated measurement error, we want to sample from the conditional distribution of the facies given the data. A relevant way to generate conditioned realizations is to use Markov chains Monte Carlo (MCMC). However, the dimensions of the model and the computational cost of each iteration are two important pitfalls for the use of MCMC. Furthermore, classical MCMC algorithms mix slowly, that is, they will not explore the whole support of the posterior in the time of the simulation. In this paper, we extend the methodology already described in a previous work to the problem of history matching of a Gaussian-related lithofacies reservoir model. We first show how to drastically reduce the dimension of the problem by using a truncated Karhunen-Loeve expansion of the Gaussian random field underlying the lithofacies model. Moreover, we propose an innovative criterion of the choice of the number of components based on the connexity function. Then, we show how we improve the mixing properties of classical single MCMC, without increasing the global computational cost, by the use of parallel interacting Markov chains. Applying the dimension reduction and this innovative sampling method drastically lowers the number of iterations needed to sample efficiently from the posterior. We show the encouraging results obtained when applying the methodology to a synthetic history-matching case.

Three-dimensional regional-scale hydrostratigraphic modeling based on sequence stratigraphic methods: a case study of the Miocene succession in Denmark

A sound hydrogeological model is the heart of every well-performing groundwater model. A sequence stratigraphic approach has been tested, which has been widely used in the petroleum industry since the 1980s for the construction of three-dimensional hydrogeological models for sedimentary basins. The sequence stratigraphic interpretation has been established for the Miocene succes- sion in western Denmark based on seismic profiles, gamma-ray logs, sediment descriptions and palynological analysis of borehole samples. The up to 400-m-thick Miocene succession consists of deposits originating from deep marine to terrestrial depositional environments. The succession is subdivided into six sequences, corresponding system tracts and distinct lithofacies covering the period from late Oligocene to latest late Miocene. The final model is converted into a binary three-dimensional model that shows the location, geometry and thicknesses of potential deep aquifers, and is prepared for groundwater flow modeling. The model provides a qualified geological description of the connectivity between deep sand forma- tions and the ground surface. By comparison to a traditional lithofacies model based on sediment descriptions of borehole samples, it is shown that the new method gives a more sound geological understanding, which is essential when flow paths and the vulnerability of deep aquifers are evaluated.

Model building for Chang-8 low permeability sandstone reservoir in the Yanchang formation of the Xifeng oil field

In order to build a model for the Chang-8 low permeability sandstone reservoir in the Yanchang formation of the Xifeng oil field, we studied sedimentation and diagenesis of sandstone and analyzed major factors controlling this low permeability reservoir. By doing so, we have made clear that the spatial distribution of reservoir attribute parameters is controlled by the spatial distribution of various kinds of sandstone bodies. By taking advantage of many coring wells and high quality logging data, we used regression analysis for a single well with geological conditions as constraints, to build the interpretation model for logging data and to calculate attribute parameters for a single well, which ensured accuracy of the 1-D vertical model. On this basis, we built a lithofacies model to replace the sedimentary facies model. In addition, we also built a porosity model by using a sequential Gaussian simulation with the lithofacies model as the constraint. In the end, we built a permeability model by using Markov-Bayes simulation, with the porosity attribute as the covariate. The results show that the permeability model reflects very well the relative differences between low permeability values, which is of great importance for locating high permeability zones and forecasting zones favorable for exploration and exploitation.

3D hydrocarbon migration by percolation technique in an alternate sand–shale environment described by a seismic facies classified volume

Modelling hydrocarbon migration in complex depositional environments is a difficult task. This paper illustrates a workflow that has been developed in order to make use of information at seismic resolution for migration modelling purposes. After a regional identification of the main seismic sequence boundaries and systems tracts, the first step of the workflow consists in a semi-automatic classification of the sand and shale prone seismic facies that are interpretable in the seismic volume. A software based on supervised neural network techniques and textural seismic attributes supports volumetric classification. Each class represents a different facies with specific petrophysical properties (threshold pressure, porosity, irreducible water saturation, etc.), assigned according to concurrent sedimentological studies; the result is a 3D lithofacies model. Petroleum system modelling is performed on a 3D regional model up to the calculation of hydrocarbon quantities expelled from the source rock. Then migration modelling is performed on the smaller area covered by the 3D lithofacies model. Invasion percolation approach has been chosen because it allows to perform migration modelling at a scale that is small enough to maintain a sufficient detail of the channels' shape and of their connections. A probabilistic approach allows to estimate charge risk in new prospects and to check modelling consistency. This workflow has been applied in a deep water setting in West Africa, characterized by an interconnected channel system, where a number of prospects had to be evaluated. A well was drilled in the area prior to this study, resulting dry in spite of being located in a position where the source rock is present and is deemed to have expelled interesting amounts of hydrocarbons. This study has shown that hydrocarbons expelled from the source appear to be deviated laterally by the presence of a local seal and therefore do not reach the channel system in the well location.

Tufas and travertines of the Mediterranean region: a testing ground for freshwater carbonate concepts and developments

AbstractActively forming travertine and tufa sites are widespread in temperate and warm climates, such as the Mediterranean region, but also develop in other climatic regimes from the Arctic to equatorial latitudes. The terms travertine and tufa are sometimes applied indiscriminately today; however, each has a specific definition. Most studied deposits are active or of Quaternary (especially Holocene) age. There are fewer Tertiary and even less late Mesozoic examples on record. In part, this is because of a recognition problem as these deposits commonly preserve as fragmentary erosional remnants. Physico‐chemical precipitation of calcium carbonate is commonly considered to be the main mechanism contributing to tufa and travertine deposition. Rapid cooling of waters further aids the process in thermal (travertine) sites. However, biomediation of calcium carbonate, associated with prokaryote–microphyte biofilms, is now recognized to be equally important in slow flowing and near static sites. Much of this precipitation occurs in close association with cyanobacterial, heterotrophic bacterial and diatoms. The Mediterranean region figures significantly in the development of travertine and tufa research. Consequently, this article employs key literature on Mediterranean examples to illustrate developments and concepts in freshwater carbonate research. Modern work started in the 1950s and defined many previously unknown outcrops. Many such sites became the targets for early radiocarbon dating studies. Since the late 1970s, a number of laboratory‐based and field‐based classification schemes have been proposed. Some schemes were based on associated macrovegetation types; others were based on petrological characteristics or on geometry and sedimentary facies characteristics. Of late, the sedimentological modelling has benefited greatly from the use of shallow geophysical techniques in order to better define the internal geometries of the deposits. Two distinct research strands were developed during the 1990s, both aimed at a better understanding of environmental change. The first considered biocomponents. The research was multifaceted and ranged from exploring the role of micro‐organisms in cement and lime mud precipitation to biostratigraphic analysis of contained pollen and invertebrate remains; these provided proxy environmental information which has helped to define past climate variability in the region. The second research strand focussed on geochemical signatures as a proxy tool for defining environmental change. This definition was made possible by new petrological studies which have greatly aided the recognition of primary depositional fabrics. This improved knowledge has reduced the sampling dangers of diagenetic contamination inherent in earlier geochemical studies. Lithofacies modelling has also brought to light important early post‐depositional neomorphic changes, which obscure primary fabrics, and complex diagenetic histories have been revealed. Stable isotope signatures of carbon and oxygen have also been used to define palaeo‐water temperature. Fine‐tuning of depositional time frames has also been achieved from absolute dating techniques employing radiocarbon and uranium/thorium.