Informed production optimization in hydrocarbon reservoirs

\u3cp\u3eThe exploitation of subsurface hydrocarbon reservoirs is achieved through the control of production and injection wells (i.e., by prescribing time-varying pressures and flow rates) to create conditions that make the hydrocarbons trapped in the pores of the rock formation flow to the surface. The design of production strategies to exploit these reservoirs in the most efficient way requires an optimization framework that reflects the nature of the operational decisions and geological uncertainties involved. This paper introduces a new approach for production optimization in the context of closed-loop reservoir management (CLRM) by considering the impact of future measurements within the optimization framework. CLRM enables instrumented oil fields to be operated more efficiently through the systematic use of life-cycle production optimization and computer-assisted history matching. Recently, we have proposed a methodology to assess the value of information (VOI) of measurements in such a CLRM approach a-priori, i.e. during the field development planning phase, to improve the planned history matching component of CLRM. The reasoning behind the a-priori VOI analysis unveils an opportunity to also improve our approach to the production optimization problem by anticipating the fact that additional information (e.g., production measurements) will become available in the future. Here, we show how the more conventional optimization approach can be combined with VOI considerations to come up with a novel workflow, which we refer to as informed production optimization. We illustrate the concept with a simple water flooding problem in a two-dimensional five-spot reservoir and the results obtained confirm that this new approach can lead to significantly better decisions in some cases.\u3c/p\u3

Using combinatorics to compute fluid routing alternatives in a hydrocarbon production network

Typically, hydrocarbon production networks have several fluid routing alternatives that are applied by opening and closing on–off valves. This usually sends the wells’ fluids through a specific pipeline, pump or compressor, or to a particular separator, among other requirements. This paper presents a general methodology to compute all fluid routing configurations of a production network using a graph representation of it. The particular implementation case discussed in this paper involves interacting with a preexisting steady-state computational model of the production network. The method starts by extracting from the model the name list of elements in the network and their type. Equipment (wells, separators, junctions, pumps, compressors, valves, etc.) is tagged as nodes and pipes (flowlines, connectors) are tagged as edges. The nodes are further classified by type: sources (wells), internals (e.g., junctions) or sinks (separators). The start and end element of each edge is recorded. This process yields a network connectivity list. A depth-first search is executed from each source to each sink. The search keeps track of the edges that must be active in each path and honors (if any) pre-specified edge directions. All paths for one source node in one component are combined to form all feasible edge combinations for that source node, and these combinations are again combined for all the source nodes in each component. This is repeated for all graph components. The unique combinations are stored and reported at the end. The method has been tried in a production network with seven wells representing a typical subsea production system in the North Sea where the wells have the option to produce (through two flowlines) to two separators on the platform. The production network model was available in a commercial software; thus, there was no access to the code or the underlying equations. The model was controlled from an external computational routine using automation. The graph was extracted from the model, all operating configurations of the network were computed (2187), and then each one was applied (by enabling or disabling flowlines) and evaluated in the commercial software. This allowed to identify routing configurations that provided maximum total oil production or maximum total gas production. There were only 306 configurations that yielded a total oil production close (within 10%) to the maximum recorded oil production. The input data of the production system model are given in the appendix for verification and benchmarking by a third party. Details about the implementation are provided.publishedVersion© The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Value of information in closed-loop reservoir management

This paper proposes a new methodology to perform value of information (VOI) analysis within a closed-loop reservoir management (CLRM) framework. The workflow combines tools such as robust optimization and history matching in an environment of uncertainty characterization. The approach is illustrated with two simple examples: an analytical reservoir toy model based on decline curves and a water flooding problem in a two-dimensional five-spot reservoir. The results are compared with previous work on other measures of information valuation, and we show that our method is a more complete, although also more computationally intensive, approach to VOI analysis in a CLRM framework. We recommend it to be used as the reference for the development of more practical and less computationally demanding tools for VOI assessment in real fields

Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study

Objectives Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis. Setting Prospective, international, multicentre, observational cohort study. Participants Patients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative). Primary outcome 30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality. Results This study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787). Conclusions Patients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups