Over the last decade, extensive studies have been carried out on the behaviour of vertical well tests in dual porosity systems. Although some research has been done on the horizontal well counterpart [1-51, most of this work has just dealt with a specific situation. No systematic investigation has been yet reported on the behaviour of transient horizontal well tests in dual porosity systems. One of the major purposes of this study is to bridge this gap. An approximate analytic solution in real time space modelling the horizontal well test in a dual porosity reservoir is given. Responses for both pseudo-semi-steady-state (psss) and transient dual porosity models in linear flow are investigated. For homogeneous reservoirs the theory of pressure transient analysis for horizontal wells suggests that linear flow should be the dominant flow regime when Llh is large and the loglog derivative diagnostic would frequently exhibit a half slope. However field examples quoted in this paper do not show this feature nearly as often as one mifiht anticipate and unexpectedly quarter slopes have been observed in many cases. The paper particularly illustrates how a bilinear flow can arise in the case of a horizontal well in a transient dual porosity (fractured or layered) reservoir.
This paper presents a systematic evaluation of enhanced oil recovery (EOR) potentials for St Joseph Field located in the offshore Malaysia. The field has been in production for 30 years, currently under gas injection and started injecting water in March 2011. Chemical EOR (cEOR) was identified as the most effective EOR process for maximizing ultimate recovery for St Joseph and the two nearby fields. This paper presents the key results of the St Joseph chemical EOR feasibility evaluation. It also discusses an integrated area development concept exploiting the synergies between the three fields of North Sabah, which is recognized as key to a successful cEOR development in the area. This study aimed to understand the size of prize in case of both polymer flood and Alkaline-Surfactant-Polymer (ASP) EOR scenarios using 3D full field models. One dimensional box and single well models were used to understand the physics of the EOR processes whereas the full field model was used to investigate the EOR subsurface development concepts, infill opportunities, injector/producer placements, slug size etc. It is anticipated that the proposed ASP flood will increase the ultimate recovery factor for the EOR targeted sands to circa 65%. Potential subsurface risks/uncertainties were also investigated. The chemical EOR process will involve handling a large volume of chemicals. This represents a major challenge in application of chemical EOR technologies in an offshore environment like St Joseph. Various facilities concepts were examined in detail. The selected concept is a combination of a mobile floating facility for the injection water treatment and chemical injection packages and a platform-based facility for processing the produced fluids/chemical. A pilot injection prior to full implementation has been planned to manage key subsurface/surface uncertainties and main challenges. The detailed studies of the pilot design and implementation are presented in a separate paper.
Chemical enhanced oil recovery (cEOR) is a complex process which exhibits a number of risks and uncertainties. A successful chemical EOR implementation depends on the success and the ability in addressing all these risks upfront and one of the important de-risking steps is the piloting process before full scale implementation. St Joseph is an offshore field in North Sabah region of Malaysia chosen for chemical EOR implementation. In line with the implementation of chemical EOR, there are a number of uncertainties and risks associated with such a development. Some key uncertainties are generic to cEOR development, such as the heterogeneity, chemical effectiveness, emulsion, production of sales specification oil, etc. However, there are certain risks and uncertainties that are typical for this particular field, such as fractured injection, offshore environment, large secondary gas cap, etc. In order to de-risk the full field development and get a better handle on the risks and uncertainty, a pilot is planned. The paper discuses the approach taken for the pilot selection, which includes the qualitative and quantitative assessment of pilot types vis-à-vis key risks, the workflow towards designing the pilot, the modeling approach followed, the facility concept of the design, and injectivity modeling, leading to final pilot design. The paper also touches upon the data acquisition and surveillance plan to analyze the pilot performance and quantitatively address the key risks. In addition, the risks and uncertainties of the pilot implementation are also discussed, together with the mitigation and remediation methods. The pilot study resulted in the detailed pilot design and data gathering plan. The results of the pilot will be used to determine if chemical EOR is viable for full field development at St Joseph.
It is a common practice to reduce the number of parameters that are used to fully describe a static geological model for assisted-history-matching (AHM) of geologically complex reservoirs. However, a model reconstructed from the reduced parameters may often be distorted from prior geological information, especially when discrete facies indicator presents in the model; for example, a reconstructed “channel” does not look like a channel. This paper presents a novel machine learning (ML) method that learns prior geological information/data, and then reconstructs a model after pluri-principal-component-analysis (pluri-PCA) is applied. The main steps of the methods are: first, a dictionary of object-based channelized geological models is generated based on the prior geological data/information. A pluri-PCA approach is applied to reduce the dimensions of grid-based static model and to convert the facies models to Gaussian PCA-coefficients. Second, the PCA coefficients are tuned during history matching process and the pluri-Gaussian rock-type-rule is applied to reconstruct the complex geological facies model from the tuned coefficients. Finally, a ML technique called “Piecewise Reconstruction from a Dictionary” (PRaD), which is based on the Markov Random Field method, is introduced to minimize the feature distance between the reconstructed model and the training models. In order to enforce geological plausibility, the facies models are reconstructed or regenerated by putting together pieces from different patches in the training realizations. An AHM workflow with the above described new method has been applied to a real turbidite channelized reservoir. The prior geological model indicates that there is clear sand deposition between a gas injector and oil producers. However, one of the production wells has been observed much less gas production than simulated result. Without adding the plausiable additonal fault, the AHM results convinced that the reasonable match on gas production can only be achieved by changing channel orientation and shales/facies distribution. In addition, the new method is observed to preserve both channel features and geostatistics of the model parameters (e.g. facies, permeability, porosity). The additional uncertainties in dynamic aspects (e.g. aquifer strength, relative permeability multipliers, etc.) will be included in AHM workflow and addressed by a derivative-free optimization approach. The new method is able to leverage the prior information provided by geologists in order to produce a non-Gaussian geologically plausible facies model that matches the observation data. While the pluri-PCA reconstruction process helps to preserve the major features and facies fraction within the geological model description, the PRaD method recaptures the missing details of minor features and enables the final model to closely link to the training realizations. Unlike the conventional approach, e.g. adding artificial flow barrier, this method renders the whole history matching workflow applicable to practical problems. In summary, the proposed method can further enhance the quality of the model reconstructed from a training dictionary of geological models.
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