Early Product Life Research Articles

Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms.

Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effectiveand reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modelingandpredictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risksthatcan potentially affect the drug product quality.

Early Production Life of Wheatstone Project Offshore Australia Yields Key Lessons

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202246, “Wheatstone: What We Have Learned in Early Production Life,” by John Pescod, SPE, Paul Connell, SPE, and Zhi Xia, Chevron, et al., prepared for the 2020 SPE Asia Pacific Oil and Gas Conference and Exhibition, originally scheduled to be held in Perth, Australia, 20–22 October. The paper has not been peer reviewed. Wheatstone and Iago gas fields, part of the larger Wheatstone project, commenced production in June 2017. The foundation subsea system includes nine Wheatstone and Iago development wells tied back to a central Wheatstone platform (WP) for processing. Hydrocarbons then flow through an export pipeline to an onshore processing facility that includes two liquefied-natural-gas (LNG) trains and a domestic gas facility. The complete paper highlights some of the key learnings in well and reservoir surveillance analysis and optimization (SA&O) developed using data from early production. Asset Overview Chevron Australia’s Wheatstone project is in the North West Shelf region offshore Australia (Fig. 1). Two gas fields, Wheatstone and Iago (along with a field operated by a different company), currently tie into the WP in the Northern Carnarvon Basin. These two gas fields are in water depths between 150 and 400 m. The platform processes gas and condensate through dehydration and compression facilities before export by a 220-km, 44-in., trunkline to two 4.45-million-tonnes/year LNG trains and a 200 tera-joule/day domestic gas plant. A Wheatstone/Iago subsea system consisting of two main corridors delivers production from north and south of the Wheatstone and Iago fields to the WP. Currently, the subsea system consists of nine subsea foundation development wells, three subsea production manifolds, two subsea 24-in. production flowlines, and two subsea 14-in. utility lines. The nine foundation development wells feed the subsea manifolds at rates of up to 250 MMscf/D. These wells have openhole gravel-pack completions for active sand control and permanent downhole gauges situated approximately 1000-m true vertical depth above the top porosity of multi-Darcy reservoir intervals for pressure and temperature monitoring. All wells deviate between 45 and 60° through the reservoir with stepout lengths of up to 2.5 km. The two subsea 24-in. production flowlines carry production fluids from the subsea manifolds to two separation trains on the WP. Each platform inlet production separator can handle up to 800 MMscf/D. The two 14-in. utility flowlines installed to the subsea manifolds allow routing of a single well to the platform multiuse header, which can direct flow into the multiuse separator (MUS) or other production separators at a rate of 250 MMscf/D.

Optimization of Well Sidetrack under Uncertainty using Response Surface and Decision Support. I. Primary Recovery Mechanism

The Optimal sidetrack time (tR-OPT) has been estimated for uncertainty of the probability of success (POS) of the sidetrack operation, reservoir properties and economics for a reservoir under primary recovery mechanism. The case studies worked on in literature considered in this study are for those for primary recovery in which production profiles were represented by empirical and analytical models. However, not all recovery can be adequately replicated by these analytical models. Hence, the need to apply proxy models not just to predict cumulative production but net-present-value (NPV). In this study the analysis of a decision tree with several branches is carried out to maximize NPV that is evaluated under the influence of production stoppage due to the sidetrack into another non-communicating upper zone with uncertainty of reservoir properties. The optimal sidetrack time adds a severe non-linearity in the response of the resulting proxy model and expected monetary value (EMV), the objective function. Multi -objective functions of proxy models over time-intervals for highly time impacted terminal branches, known as split design was applied to evaluate when to conduct a well sidetrack operation under risk and uncertainty in order to resolve severe non-linearity of the NPV solved by a standard optimization algorithm in a spreadsheet. The Predicted values of optimal sidetrack time by the developed workflow was relatively reasonable and highly satisfactory in comparison with simulation results and that of empirical and analytical models. Though, further performance improvement is possible, the constraint on computational time for multi-objective optimization must be weighed against the desired result. Monte Carlo implementation on EMV based on uncertainty of reservoir properties and varying POS acknowledges the fact that for favourable POS, that is values approaching 1.0, tR-OPT clustered at early production life with a spike and the later for unfavourable values.

Integration of Response Surface and Decision Support to optimize a Well Sidetrack under Uncertainty II. Secondary Recovery Mechanism

The optimal time to sidetrack into a different layer from an already producing horizon with secondary recovery mechanism of waterflooding is evaluated with the uncertainties embedded in Probabilities of success (POS) including economic, operational, technical and reservoir properties. Previous literatures are majorly primary recovery and secondary recovery by waterflooding in which production profiles were represented by empirical and analytical models. However, not all recovery mechanisms can be sufficiently reproduced by these models and this introduces and explains the need for the use of proxy models to predict cumulative production and net-present-value (NPV). The peculiarity of this study is the application of decision analysis/tree with multiple terminal branches to both production and injection sidetrack, where NPV is estimated under the influence of change of recovery mechanism due to sidetrack (recompletion) to another possibly non-communicating zone or layer with uncertainty of reservoir properties and production discontinuity from the already producing horizon. By and large, sidetrack time adds in acute non-linearity on the NPV. Multi–objective functions of proxy models over time-intervals for the impacted terminal branches, known as split design was applied to evaluate when to carry out a well sidetrack operation under risk and uncertainty. This was adopted to resolve severe non-linearity of the NPV and the multi-objective function of EMV by a standard optimization algorithm in a spreadsheet. The final results gave a satisfactory match to the simulation results. In order to get a perfect match through more improvements on performance there is a need for large computation times and the decision must be made depending on the required result. Monte- Carlo simulation analysis shows that optimal sidetrack time is at the early production life.

Random regression animal models for genetic evaluation of test-day milk yield and milking duration in Saudi dairy goats

Random regression animal model was applied for analyzing the relationships between daily milk yield (MK) and milking duration (DR) in dairy goats comparing with reviewed estimates in dairy cows. The current analyzed data involved 17345 sample test-day records from multiparous Saudi dairy goats. A cubic random regression was applied for representing additive genetic variances in all studied traits across all different days in milk (12 groups). Based on multi-lactation random regression data-set analysis, the role of inheritance was greatest during the later stages of lactation. Heritability estimates of daily milk yield (h2MK) ranged from 0.15 to 0.54. While estimates of heritability for milking duration (h2DR) were very low during the first 60 days of lactation, being not more than 0.04. During the 2nd half of lactation the estimates of h2DR ranged from 0.35 to 0.39. Results of genetic variations for lactation records during early production life showed that highest milk harvest with intermediate milking rate could be achieved. Estimates of expected breeding values for milk yield and milking duration increased in different rates with progressing days in milk groups. These results indicated that individual selection results would be favorably achieved during the late part of lactation. Additive genetic correlations between measures of all traits at different lactation months continuously decreased as the interval between test days increased. Additive genetic correlations between milking duration and milk yield were positive and considerably high. Correlations between expected breeding values of both traits ranged from 0.41 to 0.83 (mean = 0.69) across different lactation months. More details on estimates of breeding values, estimates of permanent environmental and additive genetic correlations for all traits were tabulated.

Knocking on Industry’s Door: Needs in Product-Cost Optimization in the Early Product Life Cycle Stages

While theoretical concepts for product-costing methodologies have evolved over the decades, little emphasis has been placed on their integration into modern information systems. During a co-innovation workshop at SAP SE, we initiated our collaborative research with selected large-scale enterprises from the discrete manufacturing industry. Moreover, we conducted interviews with business experts to gain a sophisticated understanding of the cost-optimization process itself. As a result, we present an exemplary optimization process with an emphasis on the specific characteristics of the product development stage. Based upon this example, we identified associated deficits in information system support. No current software fulfills the enterprises’ requirements regarding cost optimization in the early stages of a product’s life cycle. Thus, the respective processes lack integration in corporate environments. Taking this on, our article compiles detailed problem identification and, moreover, suggests approaches to overcome these hurdles.

Model Based Safety Analysis with smartIflow

Verification of safety requirements is one important task during the development of safety critical systems. The increasing complexity of systems makes manual analysis almost impossible. This paper introduces a new methodology for formal verification of technical systems with smartIflow (State Machines for Automation of Reliability-related Tasks using Information FLOWs). smartIflow is a new modeling language that has been especially designed for the purpose of automating the safety analysis process in early product life cycle stages. It builds up on experience with existing approaches. As is common practice in current approaches, components are modeled as finite state machines. However, new concepts are introduced to describe component interactions. Events play a major role for internal interactions between components as well as for external (user) interactions. Our approach to the verification of formally specified safety requirements is a two-step method. First, an exhaustive simulation creates knowledge about a great variety of possible behaviors of the system, especially including reactions on suddenly occurring (possibly intermittent) faults. In the second step, safety requirements specified in CTL (Computation Tree Logic) are verified using model checking techniques, and counterexamples are generated if these are not satisfied. The practical applicability of this approach is demonstrated based on a Java implementation using a simple Two-Tank-Pump-Consumer system.

Offering Pharmaceutical Samples: The Role of Physician Learning and Patient Payment Ability

Physicians may learn about prescription drug effectiveness directly from the firm via detailing or from patient experience. Patient-mediated learning is aided by the use of free drug samples. The effective use of samples is hampered by a lack of understanding of its exact return on investment implications. We seek to fill this gap by incorporating the physician's sample allocation behavior in the firm's decision making. We uncover the following implications for firms as well as policy makers. First, we find that the optimal sampling level for a drug category is a nonmonotonic function of patient payment ability and the price of the drug. Second, an increase in the cost of samples can lead to an increase in sampling and a decrease in detailing when the physician's propensity to provide sample subsidies is high. Third, when future market growth is expected to be high (early stage product life cycle and/or chronic drugs) and sampling efficiency is low, the use of sampling is profitable for the firm but leads to lower market coverage than when sampling is disallowed.

Industrial evolution through complementary convergence: the case of IT security

The article addresses the dynamics through which product markets become derailed from early product life cycle (PLC)-tracks and engaged in complementary convergence with other product markets or industries. We compare and contrast the theories that can explain, respectively, the PLC and the derailing of this cycle into a process of complementary convergence. The article challenges the standard industry conception and proposes a distinction between the emergent industrial sector, the emergent product market (or niche) and the industry as a more well-established entity. Based on empirical evidence from the IT security sector, two trajectories of complementary convergence are identified, the integration of different product markets in a sectoral context, and higher-end the integration of product markets into the offerings of established industries. While young ventures dominate lower-order integration, incumbents in established industries dominate higher-order integration. For both trajectories mergers and acquisitions represent a central means for realizing convergence. Copyright 2011 The Author 2011. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press.

When does the majority become a majority? Empirical analysis of the time at which main market adopters purchase the bulk of our sales

The idea of a dual-market structure in the early stages of a product's life cycle has become one of the most widely accepted ideas among new product marketing practitioners in the past decade. Concepts such as “Early Market/Main Market” and “Visionaries/Pragmatists” have entered the lexicon of high-tech executives to express the notion that the market for new products is composed of early and main markets with a discontinuity in the diffusion process in between them. Moreover, these concepts have been at least partially tested and verified in the marketing academic literature in the past few years. We extend this branch of research by investigating the timing issues in dual-market cases. We define Change-of-Dominance Time (CD-Time) as the number of years it takes main market adopters to outnumber early market adopters. We empirically investigate this timing issue on a comprehensive data set of new product sales in the consumer electronics industry. We find that regarding explanatory determinants of CD-Time, external influence, such as advertising, to the early market is the most important explanatory variable. We examine the relationship between CD-Time and other early product life cycle phenomena: Takeoff, Saddle, and Rogers' size of adopter categories. We found relatively high correlations between these phenomena and CD-Time. The answer to the question “When does the majority become a majority?” is indeed “at 16%”! In a dual-market setting, the average time at which the main market outnumbers the early market is when 16% of the market has already adopted the product. In terms of time, in 75% of the cases the majority becomes a majority in 5 to 10 years.

A comment on “Classifying early product life cycle forms via a diffusion model: Problems and prospects” by Balasubramanian and Ghosh

A comment on “Classifying early product life cycle forms via a diffusion model: Problems and prospects” by Balasubramanian and Ghosh

Classifying early product life cycle forms via a diffusion model: Problems and prospects

We empirically test the robustness of the Easingwood approach to classify early product life cycle forms for infrequently purchased major products. Our results indicate that the key classification parameters could be unstable to variations in the sample size used for estimation, thereby producing more than one classification for several products. We demonstrate that the problem of multiple classifications can be effectively addressed by using the more rigorous criteria of joint confidence regions (as opposed to point estimates) of classification parameters. The benefit of such rigorous classification is that it increases researcher confidence in Easingwood's classification system.

A mathematical model for new product diffusion: the influence of innovators and imitators

A simple mathematical model describing the diffusion of a new, infrequently purchased product is proposed. Many previous papers have reported that the differing behaviour of “innovators” and “imitators” is fundamental to the diffusion process. However, previous models have failed to represent this differing behaviour effectively. The model presented considers a new and, importantly, simple way of viewing the diffusion process for consumer durables based on a simplified representation of “innovative” and “imitative” response of adopters to certain types of product information. The resulting model can accomodate bimodal first purchase sales curves, which are found to occur quite frequently, in addition to the more traditional unimodal curves. The resulting shape of the diffusion curve, which is the early product life cycle (PLC), can be explained in terms of key dynamic parameters of the model. Data for six consumer durables are analysed to test the model's performance.

Magnus Subsea Wells: Design, Installation, and Early Operational Experience

Summary This paper describes the subsea aspects of the Magnus field development. The design and installation of the well completions and control systems are described. Problems encountered during this phase and during the early production life of the system are discussed. Some of the longer-term operational aspects are also discussed, and recommendations are made to improve subsequent subsea developments.

Early product life cycle forms for infrequently purchased major products

A system of classifying the diffusion patterns of major new products is explained. Nine distinct classes are proposed. Using diffusion data, 26 new products are each placed in their appropriate class. The goodness of fit is seen to be high and examples of most classes are found. The full flexibility of the proposed approach does seem to be necessary. Possible explanations of class membership are proposed.

The Bay Marchand Pressure Maintenance Project Unique Challenges of an Offshore, Sea-Water Injection System

The Bay Marchand Pressure Maintenance Project Unique Challenges of an Offshore, Project Unique Challenges of an Offshore, Sea-Water Injection System Extreme formation damage caused by the injection fluid; reservoir sands that broke down under injection; severe corrosion; and mechanical problems all spelled trouble in this large pressure maintenance project. Changes, based on test results, have turned it into a successful sea-water injection system. Introduction In the Bay Marchand Pressure Maintenance Project, one of the largest offshore injection systems in the U.S., sea water is injected at high pressures into deep Miocene sands. The need for pressure maintenance in this field off the coast of Louisiana was first recognized in the early 1960's. Six major reservoirs on the prolific east flank of this large salt-dome field exhibited only limited natural water influx after a period of rapid initial development. The early design of the injection system indicated large basic requirements. Injection volume exceeded 50,000 BWPD and injection pressures were anticipated to be as high as 3,650 psig. The injection wells were 11,000 to 12,000 ft deep. A series of unexpected problems developed soon after pressure maintenance began in 1963. First, reservoir sands, which had been sufficiently competent during the early production life of the field, broke down under injection. production life of the field, broke down under injection. In addition, corrosion and the pronounced pressure-rate effects on down-hole equipment were more pressure-rate effects on down-hole equipment were more severe than had been anticipated. Finally, there was severe formation damage, which later was found to be related directly to the use of sea water as an injection fluid. Description of Reservoirs The six major reservoirs under injection in Bay Marchand represent typical Miocene sand development along the flanks of a piercement-type salt dome (Fig. 1). The reservoirs overlie each other at depths between 8,300 ft subsea and 11,400 ft subsea. They are on the east flank of the structure and have an area ranging from 1,300 to 2,300 acres. Structural dip ranges from 24 degrees near the salt dome to 8 degrees at the original oil-water contact. Average net sand thicknesses range from 6 to 36 ft. Initial reservoir pressures varied from 4,600 to 5,291 psig. All but one reservoir were undersaturated initially. Reservoir temperatures vary from 182 to 197F. While initial GOR's averaged 450 scf/STB, oil gravities were between 21 and 30 degrees API. Since PVT properties varied with depth, small primary gas caps PVT properties varied with depth, small primary gas caps existed and the oil columns were undersaturated at their volumetric midpoints. Oil viscosities ranged from 1.1 to 1.9 cp, indicating favorable mobility ratios. Porosities were uniform and averaged 29 percent. However, permeabilities exhibited wide variations; three reservoirs had geometric-mean air permeabilities of less than 100 md, while the remaining sands had values up to 2,000 md. Initial water saturations exhibited a corresponding variation from 40 to 15 percent. percent. Drilling and Production History Initial development of the project sands began in Sept., 1958, with the completion of S. L. 1424 OCS-0387 S-9. Fig. 1 illustrates placement of the completions in a typical project reservoir. The U, W, X, BB and AA structures are 12-well units. JPT P. 389