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Reservoir characterization is a main objective in oil & gas exploration where permeability-thickness, skin factor, reservoir boundaries, production potential and reservoir fluids are the key values. However, sustainability currently plays an important role which often translates to zero flaring compliance. Deep Transient Testing (DTT) is a new generation wireline formation testing technique investigating higher permeability and thicker reservoir packages with increased radius of investigation (ROI) compared to traditional Interval Pressure Transient Testing (IPTT) by flowing faster and longer and by using higher resolution pressure gauges. DTT is not a replacement for Drill Stem Testing (DST), but rather an approach to bridge the information gap between IPTT and DST, often used in green fields, especially in multi reservoirs where it is costly to test all potential zones. Offset well data are used to build a preliminary numerical model for feasibility study in the target well. As soon as recorded, relevant logs such as Nuclear Magnetic Resonance (NMR) and image logs are used to select pressure, sampling, and DTT points and to derive reservoir properties and facies description to update the numerical model. Innovative workflows, from thin beds sand count and resistivity to permeability anisotropy calculation, are employed. During the DTT acquisition, real time monitoring and constant communication between parties is critical for an effective operation. In-situ reservoir fluid data and pressure transient data are constantly analyzed and integrated into 2D, and 3D space in an open and collaborative digital cloud environment. Additionally, a 3D geo-context is created using geological and petrophysical data, optionally combined to seismic information. The last step of the workflow consists of dynamically calibrating this static model to obtain insights such as well deliverability, reservoir connectivity or minimum hydrocarbons in place. Two case studies are presented to demonstrate the value of the workflow which is applied to a complex reservoir consisting of multi layers with cross flow production potential. The DTT objectives include determining the reservoir flow potential, de-risking the presence of baffles, and to estimate the volume of influence of the test. DTT analysis and history matching is used to address reservoir uncertainties for field development planning. In addition, the total DTT produced CO2 emission is compared to traditional testing methods and shows a 70-80% reduction in CO2e (CO2 equivalent) quantities. For Malaysia alone, a total of 2,130 metric tons of CO2 emissions were reduced, equivalent to 481 cars off the road annually with 3 DTT stations. This paper presents a complete digital workflow applied to several green fields in South-East Asia region that leads to successful DTT operations initiated from intelligent planning which positively impacted the field development decisions.
Reservoir characterization is a main objective in oil & gas exploration where permeability-thickness, skin factor, reservoir boundaries, production potential and reservoir fluids are the key values. However, sustainability currently plays an important role which often translates to zero flaring compliance. Deep Transient Testing (DTT) is a new generation wireline formation testing technique investigating higher permeability and thicker reservoir packages with increased radius of investigation (ROI) compared to traditional Interval Pressure Transient Testing (IPTT) by flowing faster and longer and by using higher resolution pressure gauges. DTT is not a replacement for Drill Stem Testing (DST), but rather an approach to bridge the information gap between IPTT and DST, often used in green fields, especially in multi reservoirs where it is costly to test all potential zones. Offset well data are used to build a preliminary numerical model for feasibility study in the target well. As soon as recorded, relevant logs such as Nuclear Magnetic Resonance (NMR) and image logs are used to select pressure, sampling, and DTT points and to derive reservoir properties and facies description to update the numerical model. Innovative workflows, from thin beds sand count and resistivity to permeability anisotropy calculation, are employed. During the DTT acquisition, real time monitoring and constant communication between parties is critical for an effective operation. In-situ reservoir fluid data and pressure transient data are constantly analyzed and integrated into 2D, and 3D space in an open and collaborative digital cloud environment. Additionally, a 3D geo-context is created using geological and petrophysical data, optionally combined to seismic information. The last step of the workflow consists of dynamically calibrating this static model to obtain insights such as well deliverability, reservoir connectivity or minimum hydrocarbons in place. Two case studies are presented to demonstrate the value of the workflow which is applied to a complex reservoir consisting of multi layers with cross flow production potential. The DTT objectives include determining the reservoir flow potential, de-risking the presence of baffles, and to estimate the volume of influence of the test. DTT analysis and history matching is used to address reservoir uncertainties for field development planning. In addition, the total DTT produced CO2 emission is compared to traditional testing methods and shows a 70-80% reduction in CO2e (CO2 equivalent) quantities. For Malaysia alone, a total of 2,130 metric tons of CO2 emissions were reduced, equivalent to 481 cars off the road annually with 3 DTT stations. This paper presents a complete digital workflow applied to several green fields in South-East Asia region that leads to successful DTT operations initiated from intelligent planning which positively impacted the field development decisions.
Deep transient pressure testing, using a state-of-the-art formation testing platform, allows deeper investigation into a subsurface formation compared to previous wireline-conveyed testing techniques. Given the associated interaction of the pressure with more varied geological features, numerical reservoir models and simulations are generally required to capture the reservoir or formation heterogeneity that may be encountered during a test. However, the long computation time of such numerical simulation poses challenges for some critical interpretation tasks, such as model inversion. We propose a novel method for the parameter estimation in geologically complex reservoirs by conducting Bayesian inversion with surrogate models. To account for the geology complexity, we utilize surrogate models constructed through the polynomial chaos expansion (PCE) method, to substitute for the numerical simulators. It allows simulating the pressure response in a timely manner while at the same time providing global sensitivity analysis for each uncertain parameter in the model. The Markov chain Monte Carlo (MCMC) method is then employed with the surrogate models for conducting the Bayesian inversion with pressure transient measurements. We analyze the properties of PCE surrogate models and demonstrate that, for typical pressure transient interpretation tasks, sufficiently accurate surrogates can be constructed from an ensemble of 200 to 500 numerical model evaluations. These evaluations are performed concurrently in a cloud-based environment thus reducing the time-cost for surrogate model training to less than an hour. We then perform the Bayesian inversion on the pressure measurements and effectively characterize the model parameters with their associated uncertainties via the posterior distributions. The problem of MCMC inversion is solved in a few minutes using the surrogate models. Through our studies, we demonstrate that the adoption of surrogate models considerably reduces the computation time required, allowing the Bayesian inversion to be completed within minutes, which was unachievable with the numerical simulators. Furthermore, this new method offers accurate parameter estimations and provides posterior distributions of uncertain parameters, as well as unveiling correlations among the parameters for the interpretation of measurement. These capabilities were lacking in the current inversion process utilizing numerical simulators. Finally, the new method lends itself well to workflow automation for history matching, thus reducing the workload on petrotechnical experts and addressing today's imperatives of faster and more cost-efficient field development.
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