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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 187283, “Eliminate Decision Bias in Facilities Planning,” by Z. Cristea, Stochastic Asset Management, and T. Cristea, Consultant, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–11 October. The paper has not been peer reviewed. The complete paper holds the traditional facilities-planning methodologies, heavily based on design-basis documents and biased toward the most-conservative conditions, fail to recognize the entirety of operational conditions throughout the oilfield life cycle, leading to significant residual risk and the wastage of resources in the operations stage. An integrated stochastic approach is proposed, accounting for both subsurface and surface uncertainties and their interrelations throughout field life. Introduction The authors discuss an unbiased, data-driven stochastic work flow addressing the effect of subsurface uncertainties on surface-facilities design and operational decisions. Unlike classical design approaches, in which the most-conservative values are typically used as design input variables and assembled into design-basis documents, the stochastic work flow accounts for design-input-variable distribution and combination throughout the entire system life cycle. An example case is provided in which a flow-assurance risk is managed and chemical consumption optimized in a wet-gas field development. Theory and Definitions Oil and gas engineering projects are typically processes of high variety, low volume, and intermittent productivity, and with a high rate of diversification and complexity. Conversely, oilfield-facilities operations are expected to be continuous, characterized by high volumes and low variety. This expectation is reflected in the approach toward facilities design, where single-point, “conservative” design conditions are proposed and assembled as facilities design-basis documents. This approach frequently fails to recognize the risks and uncertainties associated with oilfield developments. In the proposed work flow, deterministic models are established to account for the dependencies between design input variables {static variables [i.e., bottomhole pressure (BHP) and bottomhole temperature (BHT)]} and the desired objective [static results (i.e., chemical- injection rate)]. In the provided example, the analyzed variables change because of subsurface and surface events with different levels of uncertainty (i.e., condensate banking, lean-gas injection, water breakthrough). Stochastic algorithms are used to create probability-distribution functions (PDFs) for all analyzed design input variables (stochastic variables). Stochastic algorithms are then applied in the deterministic model, sampling from the previously defined probability distributions. Stochastic results are assembled into insightful charts and used to analyze the most-relevant variables and correlations affecting the objective function.
- Summary/Review (0.55)
- Research Report (0.49)
Abstract We will discuss issues in developing a production-injection surface facility model to optimize both oil production and water injection strategy in a mature giant oil field. Surface facility modeling was done using commercially available software, which was coupled to Saudi Aramco's in-house reservoir simulator POWERS1. Multiple integrated strategies for analyzing production can be considered with such models. In this paper, we evaluate a facility optimization perspective where many wells are rerouted between multiple gas-oil separation plants (GOSPs) to maintain adequate reservoir pressure to deliver required oil production volumes at the lowest operating cost. We also consider cases where the objective is to evaluate injection allocation strategies, which honors surface constraints, especially surface flow lines’ maximum operating pressure restrictions. Results presented in this paper include a subsurface reservoir coupled surface facility model where proposed strategies are designed to reroute wells from five existing GOSPs producing at high water cuts to two remaining GOSPs for production consolidation. Such strategy allows for an immediate cost savings, since it reduces the number of plants while at the same time producing the required volumes of oil, at reduced water cut, while at the same time maintaining reservoir sweep and recovery since wells which otherwise might have been shut-in are kept on active production. Simultaneously with the option of optimizing oil production, rerouting offers the opportunity to examine water disposal strategies since water can be injected near locations where there might be either a need for additional sweep or simply for reservoir pressure control and redistribution without compromising overall oil production. We built a surface facility model consisting of five active GOSPs with a few hundred producers and injector (disposal) wells. The surface model is coupled to an over a million-cell reservoir model, containing a sub-set of all the wells available in the POWERS simulation model. Previous work had relied on describing exclusively the production system, leaving the injection system to be handled by POWERS’ well management rules and not subject to optimization or reconfiguration based on reservoir strategies. In this study, both the production and injection model have been constructed and calibrated using the latest available field data and history matched to field performance current at the time of calibration. Model calibration has been assisted by using automated scripts that transfer the relevant production and technical data from a corporate database to the individual model well files and which provide initial estimates for appropriate calibration parameter, such as productivity index, gradient curve matching or reservoir pressure at the time of a rate test, to current or historical field data. Those initial matches are later reviewed and validated on a well by well basis prior to use for prediction runs.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (0.58)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.55)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
- Facilities Design, Construction and Operation > Processing Systems and Design > Process simulation (1.00)