A novel exploration technique using the microbial fingerprint of shallow sediment to detect hydrocarbon microseepage and predict hydrocarbon charge — An Argentinian case study

The fundamental assumption of many successful geochemical and geomicrobial technologies developed in the last 80 years is that hydrocarbons leak from subsurface accumulations vertically to the surface. Driven by buoyancy, the process involves sufficiently large volumes directly measurable or indirectly inferable from their surface expressions. Even when the additional hydrocarbons are not measurable, their presence slightly changes the environment, where complex microbial communities live, and acts as an evolutionary constraint on their development. Since the ecology of this ecosystem is very complicated, we propose to use the full-microbiome analysis of the shallow sediments samples instead of targeting a selected number of known species, and the use of machine learning for uncovering the meaningful correlations in these data. We achieve this by sequencing the microbial biomass and generating its “DNA fingerprint”, and by analyzing the abundance and distribution of the microbes over the dataset. The proposed technology uses machine learning as an accurate tool for determining the detailed interactions among the various microorganisms and their environment in the presence or absence of hydrocarbons, thus overcoming data complexity. In a proof-of-technology study, we have taken more than 1000 samples in the Neuqu謠Basin in Argentina over three distinct areas, namely, an oil field, a gas field, and a dry location outside the basin, and created several successful predictive models. A subset of randomly selected samples was kept outside of the training set and blinded by the client operator, providing the means for objectively validating the prediction performance of this methodology. Uncovering the blinded dataset after estimating the prospectivity revealed that most of these samples were correctly predicted. This very encouraging result shows that analyzing the microbial ecosystem in the shallow sediment can be an additional de-risking method for assessing hydrocarbon prospects and improving the Probability Of Success(POS) of a drilling campaign.

Visualizing Hydrocarbon Migration Pathways Associated with the Ringhorne Oil Field, Norway: An Integrated Approach

Norway’s Ringhorne Field is a faulted anticline which produces oil from Triassic (Statfjord) and Paleocene (Hermod) sands. It is located on the Utsira High. Geochemical studies of the produced oil indicate the oil is generated from mature Upper Jurassic marine shales in the adjacent Viking Graben. However, it has not been clear how oil migrated into the Triassic reservoirs and charged the overlying Paleocene reservoirs. Gas chimney detection using a proven neural network technique was used to detect the vertical hydrocarbon migration pathways on normally processed seismic data. The processing results were then validated using a set of criteria to determine if they represented true hydrocarbon migration rather than seismic artifacts. The chimney processing results using this traditional (shallow) neural network was compared with convolutional neural network (deep learning) results and geo-mechanical modeling on key lines. Key reservoirs were delineated using a stochastic (elastic) inversion approach. Reliable chimneys were then visualized in the vicinity of the producing reservoirs. The results showed pathways by which the Triassic fluvial sands received charge, and how these reservoirs had flank leakage to provide charge to shallower Paleocene reservoirs. This approach has now been used over hundreds of fields and dry holes in the Norwegian North Sea and worldwide as analogs to assess hydrocarbon charge and top seal risk predrill.

Acquire Ocean Bottom Seismic Data and Time-Lapse Geochemistry Data Simultaneously to Identify Compartmentalization and Map Hydrocarbon Movement

Current offshore hydrocarbon detection methods employ vessels to collect cores along transects over structures defined by seismic imaging which are then analyzed by standard geochemical methods. Due to the cost of core collection, the sample density over these structures is often insufficient to map hydrocarbon accumulation boundaries. Traditional offshore geochemical methods cannot define reservoir sweet spots (i.e. areas of enhanced porosity, pressure, or net pay thickness) or measure light oil or gas condensate in the C7 – C15 carbon range. Thus, conventional geochemical methods are limited in their ability to help optimize offshore field development production. The capability to attach ultrasensitive geochemical modules to Ocean Bottom Seismic (OBS) nodes provides a new capability to the industry which allows these modules to be deployed in very dense grid patterns that provide extensive coverage both on structure and off structure. Thus, both high resolution seismic data and high-resolution hydrocarbon data can be captured simultaneously. Field trials were performed in offshore Ghana. The trial was not intended to duplicate normal field operations, but rather provide a pilot study to assess the viability of passive hydrocarbon modules to function properly in real world conditions in deep waters at elevated pressures. Water depth for the pilot survey ranged from 1500 – 1700 meters. Positive thermogenic signatures were detected in the Gabon samples. A baseline (i.e. non-thermogenic) signature was also detected. The results indicated the positive signatures were thermogenic and could easily be differentiated from baseline or non-thermogenic signatures. The ability to deploy geochemical modules with OBS nodes for reoccurring surveys in repetitive locations provides the ability to map the movement of hydrocarbons over time as well as discern depletion affects (i.e. time lapse geochemistry). The combined technologies will also be able to: Identify compartmentalization, maximize production and profitability by mapping reservoir sweet spots (i.e. areas of higher porosity, pressure, & hydrocarbon richness), rank prospects, reduce risk by identifying poor prospectivity areas, accurately map hydrocarbon charge in pre-salt sequences, augment seismic data in highly thrusted and faulted areas.

Numerical Study of Hydrocarbon Charge Reduction Methods in HVAC Heat Exchangers

Required refrigerant charge in heat pump systems with propane is analyzed. Two systems are compared: the first a direct heat pump, with fin-and-tube heat exchangers, and the second an indirect system, with plate heat exchangers with an additional brine-to-air heat exchanger. Each system was considered to be able to work reversibly, with 5~kW design cooling capacity in summer and 8~kW design heating capacity in winter. Two separately developed simulation codes were used to calculate the required refrigerant charge and the efficiency of each of the systems. The charge was reduced by the use of microfinned tubes up to 22% in direct system reduced using microfinned tubes compared to the smooth tube. For the indirect system using specially designed plate heat exchangers with the minimum internal volume, their charge was reduced by up to 66% compared to normal plate heat exchangers.

Basin-to-prospect-scale subsurface characterisation: new insights into the Exmouth Sub-basin, North West Shelf, Australia

The Exmouth Sub-basin represents part of the intracratonic rift system of the northern Carnarvon Basin, Australia. Hydrocarbon exploration has resulted in the discovery of a variety of oil and gas accumulations, mainly in Upper Triassic, Upper Jurassic and Lower Cretaceous intervals. Recent 3D petroleum systems modelling aided in understanding the interaction of the complex basin evolution and hydrocarbon charge history, shedding light on the variety and distribution of hydrocarbon types encountered, whilst also highlighting future remaining potential in both proven and untested plays. As a result of this modelling, the Exmouth Subsurface Characterisation Study was commissioned to further leverage >12000km2of recently acquired and processed seismic data and integrate data from specifically conditioned wells from across the Exmouth Sub-basin. The primary study objective was to better understand the distribution of lithologies across the basin, with focus upon the reservoir presence and properties over proven and potential deeper sections. Furthermore, given the variety of hydrocarbon types encountered, this study set out to understand the amplitude behaviour of these types within the different reservoirs. Collectively, these results have aided in identifying analogous hydrocarbon amplitude responses across the basin, derisking identified plays, prospects and existing discoveries and fields whilst also identifying new plays and leads.