Novel While-drilling Workflow for Reservoir Structural Prediction Ahead of the Bit
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Cited by 5 publications
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In this work we present a systematic geosteering workflow that automatically integrates a priori information and the real-time measurements for updating of geomodel with uncertainties, and uses the latest model predictions in a Decision Support System (DSS). The DSS supports geosteering decisions by evaluating production potential versus drilling and completion risks. In our workflow, the uncertainty in the geological interpretation around the well is represented via multiple realizations of the geology. The realizations are updated using EnKF (Ensemble Kalman Filter) in real-time when new LWD measurements become available, providing a modified prediction of the geology ahead of the bit. For every geosteering decision, the most recent representation of the geological uncertainty is used as input for the DSS. It suggests steering correction or stopping, considering complete well trajectories ahead-of-the-bit against the always updated representation of key uncertainties. The optimized well trajectories and the uncertainties are presented to the users of the DSS via a GUI. This interface enables interactive adjustment of decision criteria and constraints, which are applied in a matter of seconds using advanced dynamic programming algorithms yielding consistently updated decision suggestions. To illustrate the benefits of the DSS, we consider synthetic cases for which we demonstrate the model updating and the decision recommendations. The DSS is particularly advantageous for unbiased high-quality decision making when navigating in complex reservoirs with several potential targets and significant interpretation uncertainty. The initial results demonstrate statistically optimal landing and navigating of the well in such a complex reservoir. Furthermore, the capability to adjust and re-weight the objectives provides the geosteering team with the ability to change the selected trade-offs between the objectives as they drill. Under challenging conditions, model-based results as input to a decision process that is traditionally much based on human intuition and judgement is expected to yield superior decisions. The novel DSS offers a new paradigm for geosteering where the geosteering experts control the input to the DSS by choosing decision criteria. At the same time, the DSS identifies the optimal decisions through multi-objective optimization under uncertainty. It bridges the gap between developments in formation evaluation and reservoir mapping on one side, and automation of the drilling process on the other. Hence, the approach creates value based on the existing instrumentation and technology.
In this work we present a systematic geosteering workflow that automatically integrates a priori information and the real-time measurements for updating of geomodel with uncertainties, and uses the latest model predictions in a Decision Support System (DSS). The DSS supports geosteering decisions by evaluating production potential versus drilling and completion risks. In our workflow, the uncertainty in the geological interpretation around the well is represented via multiple realizations of the geology. The realizations are updated using EnKF (Ensemble Kalman Filter) in real-time when new LWD measurements become available, providing a modified prediction of the geology ahead of the bit. For every geosteering decision, the most recent representation of the geological uncertainty is used as input for the DSS. It suggests steering correction or stopping, considering complete well trajectories ahead-of-the-bit against the always updated representation of key uncertainties. The optimized well trajectories and the uncertainties are presented to the users of the DSS via a GUI. This interface enables interactive adjustment of decision criteria and constraints, which are applied in a matter of seconds using advanced dynamic programming algorithms yielding consistently updated decision suggestions. To illustrate the benefits of the DSS, we consider synthetic cases for which we demonstrate the model updating and the decision recommendations. The DSS is particularly advantageous for unbiased high-quality decision making when navigating in complex reservoirs with several potential targets and significant interpretation uncertainty. The initial results demonstrate statistically optimal landing and navigating of the well in such a complex reservoir. Furthermore, the capability to adjust and re-weight the objectives provides the geosteering team with the ability to change the selected trade-offs between the objectives as they drill. Under challenging conditions, model-based results as input to a decision process that is traditionally much based on human intuition and judgement is expected to yield superior decisions. The novel DSS offers a new paradigm for geosteering where the geosteering experts control the input to the DSS by choosing decision criteria. At the same time, the DSS identifies the optimal decisions through multi-objective optimization under uncertainty. It bridges the gap between developments in formation evaluation and reservoir mapping on one side, and automation of the drilling process on the other. Hence, the approach creates value based on the existing instrumentation and technology.
In a deep-water green field's development, horizontal wells are drilled to exploit the reservoir with the aim of reducing drilling cost and time to first oil. During geosteering operations, Ultra Deep Azimuthal Electro-Magnetic (E.M.) measurements permit investigating the reservoir around the borehole up to a maximum depth of 30 m, in a good resistivity contrast environment. Acquired data are inverted on a vertical section, providing multi-boundary reservoir mapping along well path. With such a depth of investigation, the reservoir mapping is an excellent bridge between conventional logging-while-drilling (LWD) and seismic images. The integration of acquired wellbore data with high-resolution attributes, from seismic inversion, maximizes well placement results when operating in complex subsurface geology and expands the perspectives of geosteering application. A workflow to calibrate the reservoir structural and stratigraphyc setting has been assessed, via integration of seismic and Borehole Data. Enhancement of reservoir geometry interpretation during geosteering provides revised structural surfaces suitable for a quick update of the velocity model and a depth-calibration of all the seismic attributes used to steer wells. We describe an application of the workflow to an infill well, targeting channel and crevasse splays deposits drilled through a structurally complex oil field in the Norwegian offshore. The availability of seismic attributes (probabilities of facies and petrophysical properties) allowed improving the overall results of the well placement operation. Reservoir mapping identifies in real time Geo-bodies crossed by the well and within the range of investigation of Ultra Deep E.M. tool based on tool configuration, frequencies analysed and resistivity contrasts of the rocks. Stratigraphic correlation with offset wells, using conventional LWD data supported by Image Log interpretation, allowed allocating resistivity boundaries in terms of stratigraphic surfaces. These data are then integrated in near real time to depth calibrate maps, update the velocity model, hence the depth image of seismic attributes. After depth calibration, Geo-bodies recognized on seismic show a good correspondence with those identified on the resistivity inversion and a detailed correlation of the heterogeneous fluvial sand was possible, even in presence of minor faults. In this challenging structural and stratigraphic environment, the correlation supported decision making during well operations to target the well on the pay sand. The application proves that a detailed stratigraphic interpretation is an achievable goal in real time to steer successfully the well and to be used afterwards in a detailed reservoir model update.
Innovative reservoir seismic characterization technologies, with massive integration of Seismic and Petrophysical data, were applied for the first time, almost ten years ago, to a green field since the appraisal phase. This seismic driven static model has been continuously kept alive during development and production, effectively supporting application of innovative technologies, operations and business decision processes. Main object of this paper is to present a global overview of how different methodologies were implemented and tuned while the model evolved, taking advantages of lessons learned to improve technical knowledge and set up new opportunities. The initial model, based on five wells, was adopted to characterize the reservoir, with a robust link between lithology, elastic and dynamic properties. The innovative Petro-Elastic facies characterization, tuned at field scale for the next development phase, proved to be valid during the drilling campaign of more than twenty HA-HZ wells, with only minor adjustments required to propagate the model. An automated workflow for geostatistical simulations conditioned by seismic probability cubes of facies and petrophysical properties and multi-scenario risk analysis, including depth conversion and well calibration of seismic properties, generated Static and Dynamic models ready to be approached with a Computer Assisted History Match (CAHM)/ Ensemble based Methods. Properties distribution did not require new seismic inversion meanwhile updating the model, confirming the predictability of seismic in terms of average properties and the consistency of Rock Physics Model and Petrophysical characterization. To drill horizontal wells, geosteering activities were assessed by generating fine scale pre-job sector models. Optimization of well trajectories was not based only on seismic but on posterior probability of facies and petrophysical properties generated by a dedicated workflow. During drilling, real time integration of seismic inversion, wellbore data, and Ultra-Deep Azimuthal EM Reservoir Mapping allowed to effectively support well placement, providing real time stratigraphic interpretation of drilled sequences. The automated workflow permits now to investigate a wide distribution of realizations converging on a subset whose reservoir dynamic simulation are in agreement with the first years of production. The model represented a chance to implement approaches and workflows deployed in our reservoir department and now applied in top projects as well as in the day by day activities and still it is a platform to test new approaches and available technologies. Single innovative items have been object of more than twelve publications or conference presentations. Ten years later, we can assert that multidisciplinary integration and continuous cooperation between the subsidiary, in charge of operations, and the HQ was the key for the successful field characterization and development. For the future, this model could be a platform to test CAHM/ Ensemble based technology integrating also future 4D seismic data thanks to the initial tuned Petro Elastic facies model.
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