Geological modeling of a hydrocarbon reservoir in the northeastern Llanos Orientales basin of Colombia

An hydrocarbon reservoir was characterized via a detailed geologic model, which allowed estimation of the original oil in place. The study characterizes a hydrocarbon reservoir of two fields of unit C7 of the Carbonera Formation within the Llanos Orientales basin of Colombia. This was done using well logs, the structural surface of the regional datum of the area, segments of the Yuca fault and a local fault of the reservoir, the permeability equation, and J functions of the reservoir provided by the operating company. With this information, a two-fault model and a grid with 3D cells was created. Each cell was assigned with a value of facies and petrophysical properties: porosity, permeability, and water saturation, to obtain a 3D model of facies and petrophysical properties. Subsequently, we used the constructed models and oil-water contacts to calculate the original oil in place for each field. Field 1 has a volume of six million barrels of oil and field 2 has 9 million barrels.

DEEP TRANSIENT TESTING DIGITAL PRODUCTS CREATE NOVEL REAL-TIME RESERVOIR INSIGHTS

Offshore exploration requires the evaluation of hydrocarbon presence, estimation of volumes in place, and flow potential. To this capacity, formation testers are widely used to determine static data such as reservoir fluid gradients and reservoir pressure, obtain fluid samples, and to assess reservoir connectivity. Dynamic data, acquired with interval pressure transient testing and well testing techniques, are used to assess reserves and productivity. However, these evaluation techniques provide dynamic data at different resolution and length scales, and with different environmental footprint, cost, and operational constraints. A new wireline formation testing technique known as deep transient testing (DTT) has been introduced, which combines high-resolution measurements, higher flow rates, and longer test durations to perform transient tests in higher permeability, thicker formation, and at greater depth of investigation than with previous formation testers—without flaring and at a low carbon footprint. The platform combines advanced metrology with extensive automation to generate unique, real-time reservoir insights. Traditionally, pressure transient analysis and well deliverability predictions were produced through an analytical framework. Today, deep transient testing measurements are interpreted, and placed in reservoir context, in real-time by integration with geological and reservoir models. These steps can be performed from any wellsite utilizing cloud-based resources. Products such as reservoir fluid compressibility, saturation pressure, equation of state (EOS) models, well productivity, or minimum connected volumes are integrated in real-time interpretation utilizing numerical analysis. The digital infrastructure enables key reservoir insights to be shared between all stakeholders in a transparent and collaborative environment for both operational control and rapid decision making. This paper presents a case study where the new DTT technique was combined with numerical analysis and real-time integrated workflows to characterize a multilayer reservoir in a recent discovery in deepwater Mexico. During the drawdown phase of the DTT operation, real-time downhole fluid analysis was used to determine the fluid composition, density, viscosity, compressibility, and saturation pressure. These fluid properties were then used to generate and tune an EOS model. Accurate drawdown flow rate measurements and the subsequent pressure transients were combined with the fluid model and geologic model to enable integrated pressure transient history matching. The resulting calibrated numerical model honors the fluid measurements and geologic model and was used to predict the permeability profile, zonal producibility, and the volume of influence of the test.

Challenges of a mature Russian field's redevelopment – Advantages and disadvantages of quick-look geologic modeling

Due to the global oil price crisis in 2014, one of the MOL's preventive/reactive measures was to identify geologically or commercially risky elements within their portfolio. This involved reevaluation of all geologic data from Field A in the Volga-Urals Basin. In re-evaluating Field A, several unexpected challenges, problems and pitfalls were faced by the interdisciplinary team performing the task of building a new database, quality checking, and interpreting data dating back to 1947. To overcome these challenges related to this mature field, new approaches and fit-for-purpose methods were required in order to achieve the overall goal of obtaining a reliable estimation of remaining hydrocarbon potential. In the first phase a first-pass 3D geologic model was constructed, along with wrangling, cleaning and interpreting 70 years of subsurface data. This paper focuses on the main challenges involved in evaluating or reevaluating reservoir aspects of a mature field.The primary challenges were related to the estimation of remaining in-place hydrocarbon volumes, the optimization of infill well placement, the identification of primary and secondary well targets, the identification of critical data gaps, and the planning of new data acquisitions. The hands-on experience gained during the development of the geologic model provided invaluable information for the next steps needed in the redevelopment of the field.

Geologic Modelling Using Augmented Reality

An augmented reality (AR) system is presented which enhances the real-time collaboration of domain experts involved in the geologic modeling of complex reservoirs. An evaluation of traditional techniques is compared with this new approach. The objective of geologic modeling is to describe the subsurface as accurately and in as much detail as possible given the available data. This is necessarily an iterative process since as new wells are drilled more data becomes available which either validates current assumptions or forces a re-evaluation of the model. As the speed of reservoir development increases there is a need for expeditious updates of the subsurface model as working with an outdated model can lead to costly mistakes. Common practice is for a geologist to maintain the geologic model while working closely with other domain experts who are frequently not co-located with the geologist. Time-critical analysis can be hampered by the fact that reservoirs, which are inherently 3D objects, are traditionally viewed with 2D screens. The system presented here allows the geologic model to be rendered as a hologram in multiple locations to allow domain experts to collaborate and analyze the reservoir in real-time. Collaboration on 3D models has not changed significantly in a generation. For co-located personnel the approach is to gather around a 2D screen. For remote personnel the approach has been sharing a model through a 2D screen along with video chat. These approaches are not optimal for many reasons. Over the years various attempts have been tried to enhance the collaboration experience and have all fallen short. In particular virtual reality (VR) has been seen as a solution to this problem. However, we have found that augmented reality (AR) is a much better solution for many subtle reasons which are explored in the paper. AR has already acquired an impressive track record in various industries. AR will have applications in nearly all industries. For various historical reasons, the uptake for AR is much faster in some industries than others. It is too early to tell whether the use of augmented reality in geological applications will be transformative, however the results of this initial work are promising.

MATHEMATICAL MODEL OF SUBSTANTIATION OF THE GEOLOGICAL AND COMMERCIAL STRUCTURE FOR CONDUCTING TRIAL OPERATION AT THE “ZHANATALAP” PRODUCTION FIELD

The relevance of the problem stated in the paper is conditioned by fact that in the last decade, scientific and technological progress in the sphere of petroleum field geology was closely related to the use of advanced science-intensive geoinformational technologies based on modern database management systems. In this regard, the paper considers the general principles of constructing systems for geological and commercial field analysis and oil fields development regulation. The main goal of trial operation is to obtain direct information about the production capabilities of project sites and their geological and geophysical characteristics, sufficient to justify the optimum quantity of recoverable oil reserves and ensure effective reservoir and production engineering, as well as substantiation of the reservoir regime, identification of production facilities and assessment of the prospects for the development of oil production at the field. The leading method of this issue is the substantiation of a geologic model, which allows us to consider this problem as a purposeful and organised modelling method to improve the conduct of trial operation in the Aptian and Middle Jurassic productive horizons. The results of pressure transient analysis were obtained. The terms of trial operation and the volumes of oil production, average daily withdrawals are substantiated, the issues of equipment and technology of oil production, drilling and well development are considered.

Stimulated Reservoir Volume Characterization and Optimum Lateral Well Spacing Study of Two-Well Pad: Midland Basin Case Study

This paper introduces a flow simulation-based reservoir modeling study of a two-well pad with long production history and identical completion parameters in the Midland Basin. The study includes building geologic model, history matching, well performance prediction, and finding optimum lateral well spacing in terms of oil volume and economic metrics. The reservoir model was constructed based on a geologic model, integrating well logs, and core data near the target area. Next, a sensitivity analysis was performed on the reservoir simulation model to better understand influential parameters on simulation results. The following history matching was conducted with the satisfactory quality, less than 10% of global error, and after the model calibration ranges of history matching parameters have substantially reduced. The population-based history matching algorithm provides the ensemble of the history-matched model, and the top 50 history-matched models were selected to predict the range of Estimate Ultimate Recovery (EUR), showing that P50 of oil EUR is within the acceptable range of the deterministic EUR estimates. With the best history-matched model, we investigated lateral well spacing sensitivity of the pad in terms of the maximum recovery volume and economic benefit. The results show that, given the current completion design, the well spacing tighter than the current practice in the area is less effective regarding the oil volume recovery. However, economic metrics suggest that the additional monetary value can be realized with 150% of current development assumption. The presented workflow provides a systematic approach to find the optimum lateral well spacing in terms of volume and economic metrics per one section given economic assumptions, and the workflow can be readily repeated to evaluate spacing optimization in other acreage.

Integrating multigeophysical data to improve structural imaging in the Dayangshu Basin

We have used the integrated interpretation of gravity, magnetotelluric (MT) data, and seismic data to improve the structural imaging of the Dayangshu Basin. The Dayangshu Basin is mainly composed of clastic and volcanic rocks. The logging data in the basin show different degrees of direct hydrocarbon indication, suggesting that the Dayangshu Basin has good potential for exploration. However, the widely distributed volcanic rocks attenuate seismic waves and lead to poor seismic imaging. Thus, the seismic signal is weak in the Ganhe Formation (K1g) and reliable seismic images cannot be obtained below that formation. MT data can accurately obtain images of deep structures because the resistivity of volcanic rocks is significantly higher than that of sedimentary rocks. Therefore, to obtain a more reliable geologic model, we combine the traditional 3D MT inversion result with logging and seismic data to establish an initial model. The 3D MT fuzzy constrained inversion (FCI) produces a more reliable geophysical model and geologically meaningful results. The resistivity model inverted from FCI shows that volcanic rocks are widely distributed in the Ganhe Formation, and the resistivity value of the lower section of the Longjiang Formation is greater than that of the upper section of the Longjiang Formation. Finally, the 3D gravity inversion with structural constraints from 3D MT FCI method was performed to improve the model resolution in depth and to highlight the density variations within the Jiufengshan Formation, which can further optimize the geologic model. We have determined how the effective integration of gravity, MT, and seismic data can improve the structural imaging of the Dayangshu Basin.

Uncertainty assessment for 3D geologic modeling of fault zones based on geologic inputs and prior knowledge

Characterizing the zone of damaged and altered rock surrounding a fault surface is highly relevant to geotechnical and geo-environmental engineering works in the subsurface. Evaluating the uncertainty associated with 3D geologic modeling of these fault zones is made possible using the popular and flexible input-based uncertainty propagation approach to geologic model uncertainty assessment – termed probabilistic geomodeling. To satisfy the automation requirements of probabilistic geomodeling while still preserving the key geometry of fault zones in the subsurface, a clear and straightforward modeling approach is developed based on four geologic inputs used in implicit geologic modeling algorithms (surface trace, structural orientation, vertical termination depth and fault zone thickness). The rationale applied to identifying and characterizing the various sources of uncertainty affecting each input are explored and provided using open-source codes. In considering these sources of uncertainty, a novel model formulation is implemented using prior geologic knowledge (i.e., empirical and theoretical relationships) to parameterize modeling inputs which are typically subjectively interpreted by the modeler (e.g., vertical termination depth of fault zones). Additionally, the application of anisotropic spherical distributions to modeling disparate levels of information available regarding a fault zone's dip azimuth and dip angle is demonstrated, providing improved control over the structural orientation uncertainty envelope. The probabilistic geomodeling approach developed is applied to a simple fault zone geologic model built from historically available geologic mapping data, allowing for a visual comparison of the independent contributions of each modeling input on the combined model uncertainty, revealing that vertical termination depth and structural orientation uncertainty dominate model uncertainty at depth, while surface trace uncertainty dominates model uncertainty near the ground surface. The method is also successfully applied to a more complex fault network model containing intersecting major and minor fault zones. The impacts of the model parameterization choices, the fault zone modeling approach and the effects of fault zone interactions on the final geologic model uncertainty assessment are discussed.